Senior data scientist jobs in Gainesville, FL

Must Have Technical/Functional Skills • 5+ years of experience in data modeling, data architecture, or a similar role • Proficiency in SQL and experience with relational databases such as Oracle, SQL Server, or PostgreSQL • Experience with data modeling tools such as Erwin, IBM Infosphere Data Architect, or similar • Ability to communicate complex concepts clearly to diverse audiences Roles & Responsibilities • Design and develop conceptual, logical, and physical data models that support both operational and analytical needs • Collaborate with business stakeholders to gather requirements and translate them into scalable data models • Perform data profiling and analysis to understand data quality issues and identify opportunities for improvement • Implement best practices for data modeling, including normalization, denormalization, and indexing strategies • Lead data architecture discussions and present data modeling solutions to technical and non-technical audiences • Mentor and guide junior data modelers and data architects within the team • Continuously evaluate data modeling tools and techniques to enhance team efficiency and productivity Base Salary Range: $100,000 - $150,000 per annum TCS Employee Benefits Summary: Discretionary Annual Incentive. Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans. Family Support: Maternal & Parental Leaves. Insurance Options: Auto & Home Insurance, Identity Theft Protection. Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement. Time Off: Vacation, Time Off, Sick Leave & Holidays. Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

We are looking for a Data Scientist with expertise in optimization and forecasting to help improve how we manage labor, staffing, and operational resources across our retail locations. This role is critical in building models and decision-support tools that ensure the right people, in the right place, at the right time - balancing customer service, efficiency, and cost. You will work closely with Operations, Finance, and Store Leadership teams to deliver practical solutions that improve labor planning, scheduling, and demand forecasting. The right candidate will be confident, resourceful, and excited to own both the technical and business-facing aspects of applying data science in a fast-paced retail environment. Responsibilities Build and maintain forecasting models (time-series, machine learning, and statistical) for sales and transactions. Develop and deploy optimization models (linear/mixed-integer programming, heuristics, simulation) to improve workforce scheduling and labor allocation. Partner with operations and finance to translate forecasts into actionable staffing and labor plans that reduce costs while maintaining service levels. Build dashboards and automated tools to track forecast accuracy, labor KPIs, and staffing effectiveness. Provide insights and “what-if” scenario modeling to support strategic workforce and budget planning. Knowledge, Skills, And Abilities Strong foundation in forecasting techniques (time-series models, regression, machine learning) and optimization methods (linear/mixed-integer programming, heuristics, simulation). Proficiency in Python or R for modeling and analysis, along with strong SQL skills for working with large-scale datasets. Knowledge of statistics, probability, and applied mathematics to support predictive and prescriptive modeling. Experience building and deploying predictive models, optimization tools, and decision-support solutions that drive measurable business outcomes. Strong data storytelling and visualization skills using tools such as Power BI, Tableau, or Looker. Ability to translate analytical outputs into clear, actionable recommendations for non-technical stakeholders. Strong collaboration skills with the ability to partner cross-functionally with Operations, Finance, and Store Leadership to drive adoption of data-driven approaches. Ability to work independently and resourcefully, combining technical depth with practical problem-solving to deliver results in a fast-paced environment. Education And Requirements Required: Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Industrial Engineering, Operations Research, or related field. Minimum 2-3 years of professional experience in Data Science or a related area. Strong skills in time-series forecasting (e.g., ARIMA, Prophet, ML-based approaches). Proficiency in optimization techniques (linear programming, integer programming). Strong Python or R programming skills. SQL expertise for large, complex datasets. Strong communication skills with the ability to partner with business stakeholders. Preferred Experience in Retail, Restaurant, and/or Convenience Stores a plus. Experience with cloud platforms (Snowflake, AWS, GCP, Azure). Knowledge of BI tools (Tableau, Power BI, Looker). Physical Requirements Prolonged periods sitting/standing at a desk and working on a computer Must be able to lift up to 50 pounds Parker's is an equal opportunity employer committed to hiring a diverse workforce and sustaining an inclusive culture. Parker's does not discriminate on the basis of disability, veteran status or any other basis protected under federal, state, or local laws.

No C2C We're looking for a hands-on Data Engineer to help build, scale, and fine-tune real-time data systems using Kafka, AWS, and a modern data stack. In this role, you'll work deeply with streaming data, ETL, distributed systems, and PostgreSQL to power analytics, product innovation, and AI-driven use cases. You'll also get to work with AI/ML frameworks, automation, and MLOps tools to support advanced modeling and a highly responsive data platform. What You'll Do Design and build real-time streaming pipelines using Kafka, Confluent Schema Registry, and Zookeeper Build and manage cloud-based data workflows using AWS services like Glue, EMR, EC2, and S3 Optimize and maintain PostgreSQL and other databases with strong schema design, advanced SQL, and performance tuning Integrate AI and ML frameworks (TensorFlow, PyTorch, Hugging Face) into data pipelines for training and inference Automate data quality checks, feature generation, and anomaly detection using AI-powered monitoring and observability tools Partner with ML engineers to deploy, monitor, and continuously improve machine learning models in both batch and real-time pipelines using tools like MLflow, SageMaker, Airflow, and Kubeflow Experiment with vector databases and retrieval-augmented generation (RAG) pipelines to support GenAI and LLM initiatives Build scalable, cloud-native, event-driven architectures that power AI-driven data products What You Bring Bachelor's degree in Computer Science, Engineering, Math, or a related technical field 3+ years of hands-on data engineering experience with Kafka (Confluent or open-source) and AWS Experience with automated data quality, monitoring, and observability tools Strong SQL skills and solid database fundamentals with PostgreSQL and both traditional and NoSQL databases Proficiency in Python, Scala, or Java for pipeline development and AI integrations Experience with synthetic data generation, vector databases, or GenAI-powered data products Hands-on experience integrating ML models into production data pipelines using frameworks like PyTorch or TensorFlow and MLOps tools such as Airflow, MLflow, SageMaker, or Kubeflow

At MTech Systems, our company mission is to increase yield in protein production to help feed the growing world population without compromising animal welfare or damaging the planet. We aim to create software that delivers real-time data to the entire supply chain that allows producers to get better insight into what is happening on their farms and what they can do to responsibly improve production. MTech Systems is a prominent provider of tools for managing performance in Live Animal Protein Production. For over 30 years, MTech Systems has provided cutting-edge enterprise data solutions for all aspects of the live poultry operations cycle. We provide our customers with solutions in Business Intelligence, Live Production Accounting, Production Planning, and Remote Data Management-all through an integrated system. Our applications can currently be found running businesses on six continents in over 50 countries. MTech has built an international reputation for equipping our customers with the power to utilize comprehensive data to maximize profitability. With over 250 employees globally, MTech Systems currently has main offices in Mexico, United States, and Brazil, with additional resources in key markets around the world. MTech Systems USA's headquarters is based in Atlanta, Georgia and has approximately 90 team members in a casual, collaborative environment. Our work culture here is based on a commitment to helping our clients feed the world, resulting in a flexible and rewarding atmosphere. We are committed to maintaining a work culture that enhances collaboration, provides robust development tools, offers training programs, and allows for direct access to senior and executive management. Job Summary MTech builds customer-facing SaaS & analytics products used by global enterprise customers. You will own the database/data platform architecture that powers these products-driving performance, reliability, auditability, and cost efficiency at multi-tenant, multi-terabyte scale. Success is measured in hard outcomes: fewer P1s/support tickets, faster queries, bullet-proof ERP/SAP integrations, SLO compliance tied to SLAs, and audit ready evidence. Responsibilities and Duties Architecture & Design Own the end-to-end data architecture for enterprise SaaS (OLTP + analytical serving), including Azure SQL/MI, Databricks/Delta Lake, ADLS, Synapse/Fabric, and collaboration on Power BI semantic models (RLS, performance). Define and implement Information Lifecycle Management (ILM): hot/warm/cold tiers, 2-year OLTP retention, archive/nearline, and a BI mirror that enables rich analytics without impacting production workloads. Engineer ERP/SAP financial interfaces for idempotency, reconciliation, and traceability; design rollback/de-dup strategies and financial journal integrity controls. Govern schema evolution/DbVersions to prevent cross-customer regressions while achieving performance gains. Establish data SLOs (freshness, latency, correctness) mapped to customer SLAs; instrument monitoring/alerting and drive continuous improvement. Operations & Observability Build observability for pipelines and interfaces (logs/metrics/traces, lineage, data quality gates) and correlate application telemetry (e.g., Stackify/Retrace) with DB performance for rapid rootcause analysis. Create incident playbooks (reprocess, reconcile, rollback) and drive MTTR down across data incidents. Collaboration & Leadership Lead the DBA/DB engineering function (standards, reviews, capacity planning, HA/DR, on-call, performance/availability SLOs) and mentor data engineers. Partner with Product/Projects/BI to shape domain models that meet demanding customer reporting (e.g., Tyson Matrix) and planning needs without compromising OLTP. Required Qualifications 15+ years in data/database engineering; 5-8+ years owning data/DB architecture for customerfacing SaaS/analytics at enterprise scale. Proven results at multi-terabyte scale (≥5 TB) with measurable improvements (P1 reduction, MTTR, query latency, cost/performance). Expertise in Azure SQL/MI, Databricks/Delta Lake, ADLS, Synapse/Fabric; deep SQL, partitioning/indexing, query plans, CDC, caching, schema versioning. Audit & SLA readiness: implemented controls/evidence to satisfy SOC 1 Type 2 (or equivalent) and run environments to SLOs linked to SLAs. ERP/SAP data interface craftsmanship: idempotent, reconciled, observable financial integrations. ILM/Archival + BI mirror design for queryable archives/analytics without OLTP impact. Preferred Skills Power BI performance modeling (RLS, composite models, incremental refresh, DAX optimization). Modular monolith/microservices experience (plus, not required). Semantic tech (ontology/knowledge graphs), vector stores, and agentic AI orchestration experience (advantage, not required). EEO Statement Integrated into our shared values is MTech's commitment to diversity and equal employment opportunity. All qualified applicants will receive consideration for employment without regard to sex, age, race, color, creed, religion, national origin, disability, sexual orientation, gender identity, veteran status, military service, genetic information, or any other characteristic or conduct protected by law. MTech aims to maintain a global inclusive workplace where every person is regarded fairly, appreciated for their uniqueness, advanced according to their accomplishments, and encouraged to fulfill their highest potential. We believe in understanding and respecting differences among all people. Every individual at MTech has an ongoing responsibility to respect and support a globally diverse environment.

Sr. Data Engineer CLIENT: Fortune 150 Company; Financial Services SUMMARY DESCRIPTION: The Data Engineer will serve in a strategic role designing and managing the infrastructure that supports data storage, transforming, processing, and retrieval enabling efficient data analysis and decision-making within the organization. This position is critical as part of the Database and Analytics team responsible for design, development, and implementation of complex enterprise-level data integration and consumption solutions. It requires a highly technical, self-motivated senior engineer who will work with analysts, architects, and systems engineers to develop solutions based on functional and technical specifications that meet quality and performance requirements. Must have Experience with Microsoft Fabric. PRIMARY DUTIES AND RESPONSIBILITIES: Utilize experience in ETL tools, with at least 5 years dedicated to Azure Data Factory (ADF), to design, code, implement, and manage multiple parallel data pipelines. Experience with Microsoft Fabric, Pipelines, Mirroring, and Data Flows Gen 2 usage is required. Apply a deep understanding of data warehousing concepts, including data modeling techniques like star and snowflake schemas, SCD Type 2, Change Data Feeds, Change Data Capture. Also demonstrates hands-on experience with Data Lake Gen 2, Delta Lake, Delta Parquet files, JSON files, big data storage layers, optimize and maintain big data storage using Partitioning, V-Order, Optimize, Vacuum and other techniques. Design and optimize medallion data models, warehouses, architectures, schemas, indexing, and partitioning strategies. Collaborate with Business Insights and Analytics teams to understand data requirements and optimize storage for analytical queries. Modernize databases and data warehouses and prepare them for analysis, managing for optimal performance. Design, build, manage, and optimize enterprise data pipelines ensuring efficient data flow, data integrity, and data quality throughout the process. Automate efficient data acquisition, transformation, and integration from a variety of data sources including databases, APIs, message queues, data streams, etc. Competently performs advanced data tasks with minimal supervision, including architecting advanced data solutions, leading and coaching others, and effectively partnering with stakeholders. Interface with other technical and non-technical departments and outside vendors on assigned projects. Under the direction of the IT Management, will establish standards, policies and procedures pertaining to data governance, database/data warehouse management, metadata management, security, optimization, and utilization. Ensure data security and privacy by implementing access controls, encryption, and anonymization techniques as per data governance and compliance policies. Expertise in managing schema drift within ETL processes, ensuring robust and adaptable data integration solutions. Document data pipelines, processes, and architectural designs for future reference and knowledge sharing. Stay informed of latest trends and technologies in the data engineering field, and evaluate and adopt new tools, frameworks, and platforms (like Microsoft Fabric) to enhance data processing and storage capabilities. When necessary, implement and document schema modifications made to legacy production environment. Perform any other function required by IT Management for the successful operation of all IT and data services provided to our clients. Available nights and weekends as needed for system changes and rollouts. EDUCATION AND EXPERIENCE REQUIREMENTS: Bachelor's or Master's degree in computer science, information systems, applied mathematics, or closely related field. Minimum of ten (10) years full time employment experience as a data engineer, data architect, or equivalent required. Must have Experience with Microsoft Fabric SKILLS: Experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional and modern data integration technologies (such as ETL, ELT, MPP, data replication, change data captures, message-oriented data movement, API design, stream data integration and data virtualization) Experience working with cloud data engineering stacks (specifically Azure and Microsoft Fabric), Data Lake, Synapse, Azure Data Factory, Databricks, Informatica, Data Explorer, etc. Strong, in-depth understanding of database architecture, storage, and administration utilizing Azure stack. Deep understanding of Data architectural approaches, Data Engineering Solutions, Software Engineering principles and best practices. Working knowledge and experience with modern BI and ETL tools (Power BI, Power Automate, ADF, SSIS, etc.) Experience utilizing data storage solutions including Azure Blob storage, ADLS Gen 2. Solid understanding of relational and dimensional database principles and best practices in a client/server, thin-client, and cloud computing environment. Advanced working knowledge of TSQL and SQL Server, transactions, error handling, security and maintenance with experience writing complex stored procedures, views, and user-defined functions as well as complex functions, dynamic SQL, partitions, CDC, CDF, etc. Experience with .net scripting and understanding of API integration in a service-oriented architecture. Knowledge of reporting tools, query language, semantic models with specific experience with Power BI. Understanding of and experience with agile methodology. PowerShell scripting experience desired. Experience with Service Bus, Azure Functions, Event Grids, Event Hubs, Kafka would be beneficial. Experience working in Agile methodology. Working Conditions: Available to work evenings and/or weekends (as required). Workdays and hours are Monday through Friday 8:30 am to 5:30 pm ET.

We are seeking a ETL Databricks Data Engineer to join our team and help build robust, scalable data solutions. This role involves designing and maintaining data pipelines, optimizing ETL processes, and collaborating with cross-functional teams to ensure data integrity and accessibility. Key Responsibilities Design, develop, and maintain scalable data pipelines and ETL processes using Databricks. Create and optimize Python scripts for data transformation, automation, and integration tasks. Develop and fine-tune SQL queries for data extraction, transformation, and loading. Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data solutions. Ensure data integrity, security, and compliance with organizational standards. Participate in code reviews and contribute to best practices in data engineering. Required Skills & Qualifications 5 years of professional experience in data engineering or related roles. Strong proficiency in Databricks (including Spark-based data processing). Advanced programming skills in Python. Expertise in SQL for querying and data modeling. Familiarity with Azure Cloud and Azure Data Factory (ADF). Understanding of ETL frameworks, data governance, and performance tuning. Knowledge of CI/CD practices and version control tools (e.g., Git). Exposure to BI tools such as Power BI or Tableau for data visualization. Life at Capgemini Capgemini supports all aspects of your well-being throughout the changing stages of your life and career. For eligible employees, we offer: • Flexible work • Healthcare including dental, vision, mental health, and well-being programs • Financial well-being programs such as 401(k) and Employee Share Ownership Plan • Paid time off and paid holidays • Paid parental leave • Family building benefits like adoption assistance, surrogacy, and cryopreservation • Social well-being benefits like subsidized back-up child/elder care and tutoring • Mentoring, coaching and learning programs • Employee Resource Groups • Disaster Relief Disclaimer Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law. This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodations do not pose an undue hardship. Capgemini is committed to providing reasonable accommodations during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact. Click the following link for more information on your rights as an Applicant **************************************************************************

A leading Investment Management Firm is looking to bring on a Lead Data Engineer to join its team in Tampa, Denver, Memphis, or Southfield. This is an excellent chance to work alongside industry leaders while getting to be both hands on and helping lead the team. Key Responsibilities Project Oversight: Direct end-to-end software development activities, from initial requirements through deployment, ensuring projects meet deadlines and quality standards. Database Engineering: Architect and refine SQL queries, stored procedures, and schema designs to maximize efficiency and scalability within Oracle environments. Performance Tuning: Evaluate system performance and apply strategies to enhance data storage and retrieval processes. Data Processing: Utilize tools like Pandas and Spark for data wrangling, transformation, and analysis. Python Solutions: Develop and maintain Python-based applications and automation workflows. Pipeline Automation: Implement and manage continuous integration and delivery pipelines using Jenkins and similar technologies to optimize build, test, and release cycles. Team Development: Guide and support junior engineers, promoting collaboration and technical growth. Technical Documentation: Create and maintain comprehensive documentation for all development initiatives. Core Skills Experience: Over a decade in software engineering, with deep expertise in Python and Oracle database systems. Technical Knowledge: Strong command of SQL, Oracle, Python, Spark, Jenkins, Kubernetes, Pandas, and modern CI/CD practices. Optimization Expertise: Skilled in database tuning and applying best practices for performance. Leadership Ability: Proven track record in managing teams and delivering complex projects. Analytical Strength: Exceptional problem-solving capabilities with a data-centric mindset. Communication: Clear and effective written and verbal communication skills. Education: Bachelor's degree in Computer Science, Engineering, or equivalent professional experience. Preferred Qualifications Certifications: Professional credentials in Oracle, Python, Kubernetes, or CI/CD technologies. Agile Background: Hands-on experience with Agile or Scrum frameworks. Cloud Platforms: Familiarity with AWS, Azure, or Google Cloud services.

Job Description: GCP Data Engineer Rate: $50/hr. on W2 (No C2C) We are seeking a highly skilled GCP Data Engineer to design, build, and optimize cloud-native data pipelines and analytics solutions on Google Cloud Platform. The ideal candidate has strong experience with Python, BigQuery, Cloud Data Fusion, and core GCP services such as Cloud Composer, Cloud Storage, Cloud Functions, and Pub/Sub. This role requires a strong foundation in data warehousing concepts and scalable data engineering practices. Responsibilities Design, develop, and maintain robust ETL/ELT pipelines on Google Cloud Platform. Build and optimize data workflows using Cloud Data Fusion, BigQuery, and Cloud Composer. Write efficient and maintainable Python code to support data ingestion, transformation, and automation. Develop optimized BigQuery SQL for analytics, reporting, and large-scale data modeling. Utilize GCP services such as Cloud Storage, Pub/Sub, and Cloud Functions to build event-driven and scalable data solutions. Ensure data quality, governance, and reliability across all pipelines. Collaborate with cross-functional teams to deliver clean, trusted, production-ready datasets. Monitor, troubleshoot, and resolve performance issues in cloud data pipelines and workflows. Must-Have Skills Strong experience with GCP BigQuery (data modeling, SQL development, performance tuning). Proficiency in Python for data engineering and pipeline automation. Hands-on experience with Cloud Data Fusion for ETL/ELT development. Working experience with key GCP services: Cloud Composer Cloud Storage Cloud Functions Pub/Sub Strong understanding of data warehousing concepts, star/snowflake schemas, and best practices. Solid understanding of cloud data architecture and distributed processing. Good-to-Have Skills Experience with Vertex AI for ML pipeline integration or model deployment. Familiarity with Dataproc (Spark/Hadoop) for large-scale processing. Knowledge of CI/CD workflows, Git, and DevOps best practices. Experience with Cloud Logging/Monitoring tools.

We are At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron's progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets. Our Challenge Join our data-driven enterprise and lead the design of scalable and high-performance big data solutions. You will craft architectures that handle vast volumes of data, optimize pipeline performance, and incorporate advanced governance and AI-powered processing to unlock actionable insights. Additional Information The base salary for this position varies based on geography and other factors. In accordance with law, the base salary for this role if filled within Alpharetta, GA is $120K - 125K/year & benefits (see below). The Role Responsibilities: Design, build, and maintain scalable big data architectures supporting enterprise analytics and operational needs. Develop, implement, and optimize data pipelines using Apache Airflow, Databricks, and other relevant technologies to ensure reliable data flow and process automation. Manage and enhance data workflows for batch and real-time processing, ensuring efficiency and scalability. Collaborate with data scientists, analysts, and business stakeholders to translate requirements into robust data solutions. Implement data governance, security, and compliance best practices to protect sensitive information. Explore integrating AI/ML techniques into data pipelines, leveraging Databricks and other AI tools for predictive analytics and automation. Develop monitoring dashboards and alert systems to ensure pipeline health and performance. Stay current with emerging big data and cloud technologies, recommending best practices to improve system performance and scalability. Requirements: 5+ years of proven experience in Big Data architecture design, including distributed storage and processing frameworks such as Hadoop, Spark, and Databricks. Strong expertise in performance tuning for large-scale data systems. Hands-on experience with Apache Airflow for workflow orchestration. Proficiency in SQL for managing and querying large databases. Extensive experience with Python for scripting, automation, and data processing workflows. Experience working with cloud platforms (Azure, AWS, or GCP) preferable. Preferred, but not required: Deep understanding of data governance and security frameworks to safeguard sensitive data. Experience with integrating AI/ML models into data pipelines using Databricks MLflow or similar tools. Knowledge of containerization (Docker, Kubernetes) is a plus We offer: A highly competitive compensation and benefits package. A multinational organization with 58 offices in 21 countries and the possibility to work abroad. 10 days of paid annual leave (plus sick leave and national holidays). Maternity & paternity leave plans. A comprehensive insurance plan including medical, dental, vision, life insurance, and long-/short-term disability (plans vary by region). Retirement savings plans. A higher education certification policy. Commuter benefits (varies by region). Extensive training opportunities, focused on skills, substantive knowledge, and personal development. On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses. Coaching opportunities with experienced colleagues from our Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups. Cutting edge projects at the world's leading tier-one banks, financial institutions and insurance firms. A flat and approachable organization. A truly diverse, fun-loving, and global work culture

Our technology organization is transforming how we work at WestRock. We align with our businesses to deliver innovative solutions that: Address specific business challenges, integrate processes, and create great experiences Connect our work to shared goals that propel WestRock forward in the Digital Age Imagine how technology can advance the way we work by using disruptive technology We are looking for forward thinking technologists that can accelerate our focus areas such as building stronger foundational technology capabilities, reducing complexity, employing digital transformation concepts, and leveraging disruptive technology. As a Lead Data Engineer, you will play a pivotal role in building and scaling modern data infrastructure that powers decision-making across production, supply chain, and operations. Helps to define and analyze business requirements for Enterprise scale reports. Analyzes and evaluates business use cases for data engineering problems and helps design and develop processing solutions with ETL Cloud based technologies. How you will impact WestRock: Architect and implement scalable data pipelines using Palantir Foundry (pipelines, workshops, ontology) to unify and transform operational data. Design and develop robust data workflows using Python, Apache Airflow, and Apache Spark to support real-time and batch processing needs. Build and deploy solutions on cloud platforms (AWS or Azure), ensuring high availability, security, and performance. Collaborate with data scientists, analysts, and operations teams to deliver actionable insights and operational tooling. Define and enforce data engineering best practices, including CI/CD automation, version control (Git), and testing strategies. Mentor junior developers, conduct code reviews, and help shape the technical roadmap for the data platform. What you need to succeed: Education: Bachelor's degree in computer science or similar At least 6 years of strong Data Engineering experience Hands-on experience with Palantir Foundry, including pipelines, ontology modeling, and workshop development. Strong programming skills in Python or Java, with experience building and maintaining production-grade data pipelines. Proficiency in Apache Airflow and Apache Spark for workflow orchestration and large-scale data processing. Proven experience deploying data solutions on AWS or Azure, with strong understanding of cloud-native services. Familiarity with Git for version control and CI/CD pipelines for automated testing and deployment. Demonstrated ability to mentor junior engineers, lead projects, and work independently in a fast-paced environment. Good communication skills, with the ability to collaborate effectively across technical and non-technical teams. Good analytical and troubleshooting abilities. What we offer: Corporate culture based on integrity, respect, accountability and excellence Comprehensive training with numerous learning and development opportunities An attractive salary reflecting skills, competencies and potential A career with a global packaging company where Sustainability, Safety and Inclusion are business drivers and foundational elements of the daily work.

****************Individual Contractors (W2/1099) are encouraged to apply. Visa sponsorship is not available for this role at this time************ An Azure Data Engineer is responsible for designing, implementing, and maintaining the data infrastructure within an organization. They collaborate with both business and IT teams to understand stakeholders' needs and unlock the full potential of data. They create conceptual and logical data models, analyze structural requirements, and ensure efficient database solutions. Must Have Skills: Experience of Migrating from other platform to Databricks Proficiency in Databricks and Azure Cloud, Databricks Asset Bundles, Hoslistic vision on the Data Strategy. Proficiency in Data Streaming and Data Modeling Experience in architecting at least two large-scale big data projects Strong understanding of data scaling and its complexities Data Archiving and Purging mechanisms. Job Requirements • Degree in computer science or equivalent preferred • Demonstrable experience in architecture, design, implementation, and/or support of highly distributed applications with Azure cloud and Databricks. • 10+ Years of Hands-on experience with data modelling, database design, data mining, and segmentation techniques. • Working knowledge and experience with "Cloud Architectures" (e.g., SaaS, PaaS, IaaS) and the ability to address the unique security considerations of secure Cloud computing • Should have architected solutions for Cloud environments such as Microsoft Azure and/or GCP • Experience with debugging and performance tuning in distributed environments • Strong analytical skills with the ability to collect, organize, analyse, and broadcast significant amounts of information with attention to detail and accuracy • Experience dealing with structured, unstructured data. • Must have Python, PySpark experience. • Experience in ML or/and graph analysis is a plus

Flybridge Staffing is currently searching for a Data Engineer for a client located in the Palm Beach Gardens area. This is a direct-hire position that will work off a hybrid schedule of 2 days remote. This person will design systems that supply high-performance datasets for advanced analytics. Experience: BA degree and 5+ years of Data Engineering experience Strong experience building ETL data pipelines for on-premises SQL Server 2017 or newer Deep understanding of the development of data pipelines with either SSIS or Python Broad experience with SQL Server, including Columnstore, etc. Extensive experience using SSMS and T-SQL to create and maintain SQL Server tables, views, functions, stored procedures, and user-defined table types. Experience with data modeling indexes, Temporal tables, CLR, and Service Broker. Experience in partitioning tables and indexes, and performance improvement with Query Analyzer Experience writing C#, PowerShell, and Python. Experience with source control integration with GitHub, BitBucket, and Azure DevOps. Experience working in an Agile and Kanban SDLC. Experience with cloud-based data management solutions such as Snowflake, Redshift. Experience with Python programming is a plus. Libraries such as Pandas, Numpy, csv, Traceback, JSON, PyODBC, Math-Are nice to have. Experience writing design documentation such as ERDs, Data Flow Diagrams, and Process Flow Diagrams. Experience with open-source database engines such as Clickhouse, ArcticDB, and PostGreSQL is a plus. Responsibilities: Collaborate effectively with Stakeholders, Project Managers, Software Engineers, Data Analysts, QA Analysts, DBAs, and Data Engineers. Build and maintain data pipelines based on functional and non-functional requirements. Proactively seek out information and overcome obstacles to deliver projects efficiently. Ensure that data pipelines incorporate best practices related to performance, scaling, extensibility, fault tolerance, instrumentation, and maintainability. Ensure that data pipelines are kept simple and not overly engineered. Produce and maintain design and operational documentation. Analyze complex data problems and engineer elegant solutions. ****NO SPONSORSHIP AVAILABLE**** US Citizen, GC, EAD only please. If your background aligns with the above details and you would like to learn more, please submit your resume to jobs@flybridgestaffing.com or on our website, www.flybridgestaffing.com and one of our recruiters will be in touch with you ASAP. Follow us on LinkedIn to keep up with all our latest job openings and referral program.

looking for a Sr. Data Engineer (SQL+Python+AWS) to work on a 12+ Months, Contract (potential Extension or may Convert to Full-time) = Hybrid at St. Petersburg, FL 33716 with a Direct Financial Client = only on W2 for US Citizen or Green Card Holders. Notes from the Hiring Manager: • Setting up Python environments and data structures to support the Data Science/ML team. • No prior Data Science or Machine Learning experience required. • Role involves building new data pipelines and managing file-loading connections. • Strong SQL skills are essential. • Contract-to-hire position. • Hybrid role based in St. Pete, FL (33716) only. Duties: This role is building and maintaining data pipelines that connect Oracle-based source systems to AWS cloud environments, to provide well-structured data for analysis and machine learning in AWS SageMaker. It includes working closely with data scientists to deliver scalable data workflows as a foundation for predictive modeling and analytics. • Develop and maintain data pipelines to extract, transform, and load data from Oracle databases and other systems into AWS environments (S3, Redshift, Glue, etc.). • Collaborate with data scientists to ensure data is prepared, cleaned, and optimized for SageMaker-based machine learning workloads. • Implement and manage data ingestion frameworks, including batch and streaming pipelines. • Automate and schedule data workflows using AWS Glue, Step Functions, or Airflow. • Develop and maintain data models, schemas, and cataloging processes for discoverability and consistency. • Optimize data processes for performance and cost efficiency. • Implement data quality checks, validation, and governance standards. • Work with DevOps and security teams to comply with RJ standards. Skills: Required: • Strong proficiency with SQL and hands-on experience working with Oracle databases. • Experience designing and implementing ETL/ELT pipelines and data workflows. • Hands-on experience with AWS data services, such as S3, Glue, Redshift, Lambda, and IAM. • Proficiency in Python for data engineering (pandas, boto3, pyodbc, etc.). • Solid understanding of data modeling, relational databases, and schema design. • Familiarity with version control, CI/CD, and automation practices. • Ability to collaborate with data scientists to align data structures with model and analytics requirements Preferred: • Experience integrating data for use in AWS SageMaker or other ML platforms. • Exposure to MLOps or ML pipeline orchestration. • Familiarity with data cataloging and governance tools (AWS Glue Catalog, Lake Formation). • Knowledge of data warehouse design patterns and best practices. • Experience with data orchestration tools (e.g., Apache Airflow, Step Functions). • Working knowledge of Java is a plus. Education: B.S. in Computer Science, MIS or related degree and a minimum of five (5) years of related experience or combination of education, training and experience.

5 days onsite in Alpharetta, GA Skills required: Python Data Pipeline Data Analysis Data Modeling Must have solid Cloud experience AI/ML Strong problem-solving skills Strong Communication skill A problem solver with ability to analyze and research complex issues and problems; and proposing actionable solutions and/or strategies. Solid understanding and hands on experience with major cloud platforms. Experience in designing and implementing data pipelines. Must have experience with one of the following: GCP, AWS OR Azure - MUST have the drive to learn GCP.

Company: Toorak Capital Partners is an integrated correspondent lending and table funding platform that acquires business purpose residential, multifamily and mixed-use loans throughout the U.S. and the United Kingdom. Headquartered in Tampa, FL., Toorak Capital Partners acquires these loans directly from a network of private lenders on a correspondent basis. Summary: The role of the Lead Data Engineer is to develop, implement, for building high performance, scalable data solution to support Toorak's Data Strategy Lead Data architecture for Toorak Capital. Lead efforts to create API framework to use data across customer facing and back office applications. Establish consistent data standards, reference architectures, patterns, and practices across the organization for both OLTP and OLAP (Data warehouse, Data Lake house) MDM and AI / ML technologies Lead sourcing and synthesis of Data Standardization and Semantics discovery efforts turning insights into actionable strategies that will define the priorities for the team and rally stakeholders to the vision Lead the data integration and mapping efforts to harmonize data. Champion standards, guidelines, and direction for ontology, data modeling, semantics and Data Standardization in general at Toorak. Lead strategies and design solutions for a wide variety of use cases like Data Migration (end-to-end ETL process), database optimization, and data architectural solutions for Analytics Data Projects Required Skills: Designing and maintaining the data models, including conceptual, logical, and physical data models 5+ years of experience using NoSQL systems like MongoDB, DynamoDB and Relational SQL Database systems (PostgreSQL) and Athena 5+ years of experience on Data Pipeline development, ETL and processing of structured and unstructured data 5+ years of experience in large scale real-time stream processing using Apache Flink or Apache Spark with messaging infrastructure like Kafka/Pulsar Proficiency in using data management tools and platforms, such as data cataloging software, data quality tools), and data governance platforms Experience with Big Query, SQL Mesh(or similar SQL-based cloud platform). Knowledge of cloud platforms and technologies such as Google Cloud Platform, Amazon Web Services. Strong SQL skills. Experience with API development and frameworks. Knowledge in designing solutions with Data Quality, Data Lineage, and Data Catalogs Strong background in Data Science, Machine Learning, NLP, Text processing of large data sets Experience in one or more of the following: Dataiku, DataRobot, Databricks, UiPath would be nice to have. Using version control systems (e.g., Git) to manage changes to data governance policies, procedures, and documentation Ability to rapidly comprehend changes to key business processes and the impact on overall Data framework. Flexibility to adjust to multiple demands, shifting priorities, ambiguity, and rapid change. Advanced analytical skills. High level of organization and attention to detail. Self-starter attitude with the ability to work independently. Knowledge of legal, compliance, and regulatory issues impacting data. Experience in finance preferred.

Job Title: Data Engineer - AI/ML Pipelines Work Model: Hybrid Duration: CTH The Data Engineer - AI/ML Pipelines plays a key role in designing, building, and maintaining scalable data infrastructure that powers analytics and machine learning initiatives. This position focuses on developing production-grade data pipelines that support end-to-end ML workflows-from data ingestion and transformation to feature engineering, model deployment, and monitoring. The ideal candidate has hands-on experience working with operational systems such as Warehouse Management Systems (WMS) or ERP platforms, and is comfortable partnering closely with data scientists, ML engineers, and operational stakeholders to deliver high-quality, ML-ready datasets. Key Responsibilities ML-Focused Data Engineering Build, optimize, and maintain data pipelines specifically designed for machine learning workflows. Collaborate with data scientists to develop feature sets, implement data versioning, and support model training, evaluation, and retraining cycles. Participate in initiatives involving feature stores, model input validation, and monitoring of data quality feeding ML systems. Data Integration from Operational Systems Ingest, normalize, and transform data from WMS, ERP, telemetry, and other operational data sources. Model and enhance operational datasets to support real-time analytics and predictive modeling use cases. Pipeline Automation & Orchestration Build automated, reliable, and scalable pipelines using tools such as Azure Data Factory, Airflow, or Databricks Workflows. Ensure data availability, accuracy, and timeliness across both batch and streaming systems. Data Governance & Quality Implement validation frameworks, anomaly detection, and reconciliation processes to ensure high-quality ML inputs. Support metadata management, lineage tracking, and documentation of governed, auditable data flows. Cross-Functional Collaboration Work closely with data scientists, ML engineers, software engineers, and business teams to gather requirements and deliver ML-ready datasets. Translate modeling and analytics needs into efficient, scalable data architecture solutions. Documentation & Mentorship Document data flows, data mappings, and pipeline logic in a clear, reproducible format. Provide guidance and mentorship to junior engineers and analysts on ML-focused data engineering best practices. Required Qualifications Technical Skills Strong experience building ML-focused data pipelines, including feature engineering and model lifecycle support. Proficiency in Python, SQL, and modern data transformation tools (dbt, Spark, Delta Lake, or similar). Solid understanding of orchestrators and cloud data platforms (Azure, Databricks, etc.). Familiarity with ML operations tools such as MLflow, TFX, or equivalent frameworks. Hands-on experience working with WMS or operational/logistics data. Experience 5+ years in data engineering, with at least 2 years directly supporting AI/ML applications or teams. Experience designing and maintaining production-grade pipelines in cloud environments. Proven ability to collaborate with data scientists and translate ML requirements into scalable data solutions. Education & Credentials Bachelor's degree in Computer Science, Data Engineering, Data Science, or a related field (Master's preferred). Relevant certifications are a plus (e.g., Azure AI Engineer, Databricks ML, Google Professional Data Engineer). Preferred Qualifications Experience with real-time ingestion using Kafka, Kinesis, Event Hub, or similar. Exposure to MLOps practices and CI/CD for data pipelines. Background in logistics, warehousing, fulfillment, or similar operational domains.

Job Description Title: Senior Data Scientist Reports to: VP Credit Decision Sciences About the Company At BMG Money, our mission is to provide access to affordable and responsible credit for underserved consumers facing unexpected expenses. We all share one vision- Redefining lending through technology, where underserved individuals can thrive financially through forward-thinking, responsible, and innovative financial solutions. Job Summary We are seeking an experienced and highly capable Data Scientist to join our team at BMG Money. This senior-level career role is crucial for transforming large, complex datasets into strategic insights that drive decision-making across various functions. The successful candidate will be responsible for independently developing, validating, and supporting the implementation of predictive models, machine learning algorithms, and advanced dashboards. This role requires strong technical mastery and the ability to work collaboratively to deliver high-impact solutions. Key Responsibilities Develop, validate, and support the deployment of predictive models and machine learning algorithms, ensuring they meet rigorous standards for accuracy and business relevance. Collect, process, and analyze massive volumes of structured and unstructured data, providing technical expertise within key data domains. Design and deliver insightful dashboards, reports, and data visualizations for business, product, and technology stakeholders, clearly articulating findings and their implications. Proactively identify patterns, trends, and business opportunities hidden within the data, translating findings into proposed solutions and actionable analysis. Support other data-driven projects such as fraud detection and portfolio analysis. Ensure compliance with regulatory requirements and internal policies related to credit risk modeling. Collaborate closely with product, engineering, and business teams to integrate data-driven solutions into production systems. Ensure the quality, consistency, and integrity of all utilized datasets and contribute to establishing best practices for data quality. Mentor junior data analysts on best practices for data cleaning, modeling techniques, and analytical rigor. Research, evaluate, and apply new data analysis techniques, tools, and technologies (e.g., cloud platforms, new programming libraries). Stay current with the latest technological advancements in the Data Science field to recommend process and equipment improvements. Provide hands-on support and guidance to junior analysts, ensuring access to high-quality, well-structured data sources. Support the manager on all related matters, discussing complex issues and recommending well-researched solutions to inform procedural and strategic actions. Qualifications Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field. Minimum of 4+ years of professional experience in data analysis, data modeling, and validation of analytical models in the financial services industry. Advanced SQL proficiency (writing complex, optimized queries) Business Intelligence (BI) tools (e.g., Tableau, Power BI, Looker) Advanced Statistical and Mathematical aptitude with experience in experiment design (e.g., A/B testing) Advanced proficiency in Python or R and their data science ecosystems (e.g., Pandas, Scikit-learn). Advanced Microsoft Excel Strong Critical Thinking and Analytical Skills • Demonstrated Decision-Making Capacity Excellent Communication and Presentation Skills (ability to present technical findings clearly to a business audience) Strong Organizational and Planning Skills Proven ability to work effectively in a team environment and mentor peers/junior staff. Preferred Qualifications Master's degree or post-graduate specialization in a relevant field. Experience with Credit Risk Management. Prior experience contributing to model deployment/MLOps processes. Advanced English proficiency (for documentation/global teams). Familiarity with cloud platforms (AWS, Azure, or GCP). Foundational understanding of credit risk management

We are passionate people with an expert understanding of the digital consumer, data sciences, global telecom business, and emerging financial services. And we believe that we can make the world a better place. Job Description Looking for candidate making a career in Data Science with experience applying advanced statistics, data mining and machine learning algorithms to make data-driven predictions using programming languages like Python (including: Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn), SQL (Postgresql). Experience with ElasticSearch, information/document retrieval, natural language processing is a plus. Experience with various machine learning methods (classification, clustering, natural language processing, ensemble methods, outlier analysis) and parameters that affect their performance also helps. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. Qualifications Qualifications · Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) · At least 2 years' of experience in quantitative analytics or data modeling · Some understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms · Fluency in these programming languages (Python, SQL), Javascript/HTML/CSS/Web Development nice to have. · Familiarity with data science frameworks and visualization tools (Pandas, Visualizations (matplotlib, altair, etc), Jupyter Notebooks) Additional Information Responsibilities · Analyze raw data: assessing quality, cleansing, structuring for downstream processing · Design accurate and scalable prediction algorithms · Collaborate with engineering team to bring analytical prototypes to production · Generate actionable insights for business improvements tion will be kept confidential according to EEO guidelines.

As a Data Scientist, you will work in teams addressing statistical, machine learning, and artificial intelligence problems in a commercial technology and consultancy development environment. You will be part of a data science or cross-disciplinary team driving AI business solutions involving large, complex data sets. Potential application areas include time series forecasting, machine learning regression and classification, root cause analysis (RCA), simulation and optimization, large language models, and computer vision. The ideal candidate will be responsible for developing and deploying machine learning models in production environments. This role requires a strong technical background, excellent problem-solving skills, and the ability to work collaboratively with data engineers, analysts, and other stakeholders. Roles and Responsibilities: * Design, develop, and deploy machine learning models and algorithms under guidance from senior team members * Develop, verify, and validate analytics to address customer needs and opportunities. * Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics. * Develop and maintain pipelines for Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). * Ensure efficient data retrieval and augmentation processes to support LLM training and inference. * Participate in Data Science Workouts to shape Data Science opportunities and identify opportunities to use data science to create customer value. * Perform exploratory and targeted data analyses using descriptive statistics and other methods. * Work with data engineers on data quality assessment, data cleansing, data analytics, and model productionization * Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes. * Communicate methods, findings, and hypotheses with stakeholders. Minimum Qualifications: * Bachelor's Degree in Computer Science or "STEM" Majors (Science, Technology, Engineering and Math) with a minimum of 2+ years of experience * 2 years of proficiency in Python. * Familiarity with statistical machine learning techniques as applied to business problems * Strong analytical and problem-solving skills. * Strong communication and collaboration skills. * Note: Military experience is equivalent to professional experience Eligibility Requirement: * Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job. Desired Characteristics: * Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their machine learning services. * Experience with handling unstructured data, including images, videos, and text * Ability to work in a fast-paced, dynamic environment. * Experience with data preprocessing and augmentation tools. * Demonstrated experience applying critical thinking and problem-solving * Demonstrated experience working in team settings in various roles * Strong presentation and communications skills Note: To comply with US immigration and other legal requirements, it is necessary to specify the minimum number of years' experience required for any role based within the USA. For roles outside of the USA, to ensure compliance with applicable legislation, the JDs should focus on the substantive level of experience required for the role and a minimum number of years should NOT be used. This Job Description is intended to provide a high level guide to the role. However, it is not intended to amend or otherwise restrict/expand the duties required from each individual employee as set out in their respective employment contract and/or as otherwise agreed between an employee and their manager. This role requires access to U.S. export-controlled information. Therefore, employment will be contingent upon the ability to prove that you meet the status of a U.S. Person as one of the following: U.S. lawful permanent resident, U.S. Citizen, have been granted asylee or refugee status (i.e., a protected individual under the Immigration and Naturalization Act, 8 U.S.C. 1324b(a)(3)). Additional Information GE Aerospace offers a great work environment, professional development, challenging careers, and competitive compensation. GE Aerospace is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Aerospace will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: No