Data scientist jobs in California

Key Responsibilities Design and develop marketing-focused machine learning models, including: Customer segmentation Propensity, churn, and lifetime value (LTV) models Campaign response and uplift models Attribution and marketing mix models (MMM) Build and deploy NLP solutions for: Customer sentiment analysis Text classification and topic modeling Social media, reviews, chat, and voice-of-customer analytics Apply advanced statistical and ML techniques to solve real-world business problems. Work with structured and unstructured data from multiple marketing channels (digital, CRM, social, email, web). Translate business objectives into analytical frameworks and actionable insights. Partner with stakeholders to define KPIs, success metrics, and experimentation strategies (A/B testing). Optimize and productionize models using MLOps best practices. Mentor junior data scientists and provide technical leadership. Communicate complex findings clearly to technical and non-technical audiences. Required Skills & Qualifications 7+ years of experience in Data Science, with a strong focus on marketing analytics. Strong expertise in Machine Learning (supervised & unsupervised techniques). Hands-on experience with NLP techniques, including: Text preprocessing and feature extraction Word embeddings (Word2Vec, GloVe, Transformers) Large Language Models (LLMs) is a plus Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Experience with SQL and large-scale data processing. Strong understanding of statistics, probability, and experimental design. Experience working with cloud platforms (AWS, Azure, or GCP). Ability to translate data insights into business impact. Nice to Have Experience with marketing automation or CRM platforms. Knowledge of MLOps, model monitoring, and deployment pipelines. Familiarity with GenAI/LLM-based NLP use cases for marketing. Prior experience in consumer, e-commerce, or digital marketing domains. EEO Centraprise is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.

STAND 8 provides end to end IT solutions to enterprise partners across the United States and with offices in Los Angeles, New York, New Jersey, Atlanta, and more including internationally in Mexico and India We are seeking a highly analytical and technically skilled Data Scientist to transform complex, multi-source data into unified, actionable insights used for executive reporting and decision-making. This role requires expertise in business intelligence design, data modeling, metadata management, data integrity validation, and the development of dashboards, reports, and analytics used across operational and strategic environments. The ideal candidate thrives in a fast-paced environment, demonstrates strong investigative skills, and can collaborate effectively with technical teams, business stakeholders, and leadership. Essential Duties & Responsibilities As a Data Scientist, participate across the full solution lifecycle: business case, planning, design, development, testing, migration, and production support. Analyze large and complex datasets with accuracy and attention to detail. Collaborate with users to develop effective metadata and data relationships. Identify reporting and dashboard requirements across business units. Determine strategic placement of business logic within ETL or metadata models. Build enterprise data warehouse metadata/semantic models. Design and develop unified dashboards, reports, and data extractions from multiple data sources. Develop and execute testing methodologies for reports and metadata models. Document BI architecture, data lineage, and project report requirements. Provide technical specifications and data definitions to support the enterprise data dictionary. Apply analytical skills and Data Science techniques to understand business processes, financial calculations, data flows, and application interactions. Identify and implement improvements, workarounds, or alternative solutions related to ETL processes, ensuring integrity and timeliness. Create UI components or portal elements (e.g., SharePoint) for dynamic or interactive stakeholder reporting. As a Data Scientist, download and process SQL database information to build Power BI or Tableau reports (including cybersecurity awareness campaigns). Utilize SQL, Python, R, or similar languages for data analysis and modeling. Support process optimization through advanced modeling, leveraging experience as a Data Scientist where needed. Required Knowledge & Attributes Highly self-motivated with strong organizational skills and ability to manage multiple verbal and written assignments. Experience collaborating across organizational boundaries for data sourcing and usage. Analytical understanding of business processes, forecasting, capacity planning, and data governance. Proficient with BI tools (Power BI, Tableau, PBIRS, SSRS, SSAS). Strong Microsoft Office skills (Word, Excel, Visio, PowerPoint). High attention to detail and accuracy. Ability to work independently, demonstrate ownership, and ensure high-quality outcomes. Strong communication, interpersonal, and stakeholder engagement skills. Deep understanding that data integrity and consistency are essential for adoption and trust. Ability to shift priorities and adapt within fast-paced environments. Required Education & Experience Bachelor's degree in Computer Science, Mathematics, or Statistics (or equivalent experience). 3+ years of BI development experience. 3+ years with Power BI and supporting Microsoft stack tools (SharePoint 2019, PBIRS/SSRS, Excel 2019/2021). 3+ years of experience with SDLC/project lifecycle processes 3+ years of experience with data warehousing methodologies (ETL, Data Modeling). 3+ years of VBA experience in Excel and Access. Strong ability to write SQL queries and work with SQL Server 2017-2022. Experience with BI tools including PBIRS, SSRS, SSAS, Tableau. Strong analytical skills in business processes, financial modeling, forecasting, and data flow understanding. Critical thinking and problem-solving capabilities. Experience producing high-quality technical documentation and presentations. Excellent communication and presentation skills, with the ability to explain insights to leadership and business teams. Benefits Medical coverage and Health Savings Account (HSA) through Anthem Dental/Vision/Various Ancillary coverages through Unum 401(k) retirement savings plan Paid-time-off options Company-paid Employee Assistance Program (EAP) Discount programs through ADP WorkforceNow Additional Details The base range for this contract position is $73 - $83 / per hour, depending on experience. Our pay ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hires of this position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Qualified applicants with arrest or conviction records will be considered About Us STAND 8 provides end-to-end IT solutions to enterprise partners across the United States and globally with offices in Los Angeles, Atlanta, New York, Mexico, Japan, India, and more. STAND 8 focuses on the "bleeding edge" of technology and leverages automation, process, marketing, and over fifteen years of success and growth to provide a world-class experience for our customers, partners, and employees. Our mission is to impact the world positively by creating success through PEOPLE, PROCESS, and TECHNOLOGY. Check out more at ************** and reach out today to explore opportunities to grow together! By applying to this position, your data will be processed in accordance with the STAND 8 Privacy Policy.

We're working with a Series A health tech start-up pioneering a revolutionary approach to healthcare AI, developing neurosymbolic systems that combine statistical learning with structured medical knowledge. Their technology is being adopted by leading health systems and insurers to enhance patient outcomes through advanced predictive analytics. We're seeking Machine Learning Engineers who excel at the intersection of data science, modeling, and software engineering. You'll design and implement models that extract insights from longitudinal healthcare data, balancing analytical rigor, interpretability, and scalability. This role offers a unique opportunity to tackle foundational modeling challenges in healthcare, where your contributions will directly influence clinical, actuarial, and policy decisions. Key Responsibilities Develop predictive models to forecast disease progression, healthcare utilization, and costs using temporal clinical data (claims, EHR, laboratory results, pharmacy records) Design interpretable and explainable ML solutions that earn the trust of clinicians, actuaries, and healthcare decision-makers Research and prototype innovative approaches leveraging both classical and modern machine learning techniques Build robust, scalable ML pipelines for training, validation, and deployment in distributed computing environments Collaborate cross-functionally with data engineers, clinicians, and product teams to ensure models address real-world healthcare needs Communicate findings and methodologies effectively through visualizations, documentation, and technical presentations Required Qualifications Strong foundation in statistical modeling, machine learning, or data science, with preference for experience in temporal or longitudinal data analysis Proficiency in Python and ML frameworks (PyTorch, JAX, NumPyro, PyMC, etc.) Proven track record of transitioning models from research prototypes to production systems Experience with probabilistic methods, survival analysis, or Bayesian inference (highly valued) Bonus Qualifications Experience working with clinical data and healthcare terminologies (ICD, CPT, SNOMED CT, LOINC) Background in actuarial modeling, claims forecasting, or risk adjustment methodologies

Lead Data Scientist - Computer Vision/Image Processing About the Role We are seeking a Lead Data Scientist to drive the strategy and execution of data science initiatives, with a particular focus on computer vision systems & image processing techniques. The ideal candidate has deep expertise in image processing techniques including Filtering, Binary Morphology, Perspective/Affine Transformation, Edge Detection. Responsibilities Solid knowledge of computer vision programs and image processing techniques: Filtering, Binary Morphology, Perspective/Affine Transformation, Edge Detection Strong understanding of machine learning: Regression, Supervised and Unsupervised Learning Proficiency in Python and libraries such as OpenCV, NumPy, scikit-learn, TensorFlow/PyTorch. Familiarity with version control (Git) and collaborative development practices

The Principal Data Scientist works to establish a comprehensive Data Science Program to advance data-driven decision-making, streamline operations, and fully leverage modern platforms including Databricks, or similar, to meet increasing demand for predictive analytics and AI solutions. The Principal Data Scientist will guide program development, provide training and mentorship to junior members of the team, accelerate adoption of advanced analytics, and build internal capacity through structured mentorship. The Principal Data Scientist will possess exceptional communication abilities, both verbal and written, with a strong customer service mindset and the ability to translate complex concepts into clear, actionable insights; strong analytical and business acumen, including foundational experience with regression, association analysis, outlier detection, and core data analysis principles; working knowledge of database design and organization, with the ability to partner effectively with Data Management and Data Engineering teams; outstanding time management and organizational skills, with demonstrated success managing multiple priorities and deliverables in parallel; a highly collaborative work style, coupled with the ability to operate independently, maintain focus, and drive projects forward with minimal oversight; a meticulous approach to quality, ensuring accuracy, reliability, and consistency in all deliverables; and proven mentorship capabilities, including the ability to guide, coach, and upskill junior data scientists and analysts. 5+ years of professional experience leading data science initiatives, including developing machine learning models, statistical analyses, and end-to-end data science workflows in production environments. 3+ years of experience working with Databricks and similar cloud-based analytics platforms, including notebook development, feature engineering, ML model training, and workflow orchestration. 3+ years of experience applying advanced analytics and predictive modeling (e.g., regression, classification, clustering, forecasting, natural language processing). 2+ years of experience implementing MLOps practices, such as model versioning, CI/CD for ML, MLflow, automated pipelines, and model performance monitoring. 2+ years of experience collaborating with data engineering teams to design data pipelines, optimize data transformations, and implement Lakehouse or data warehouse architectures (e.g., Databricks, Snowflake, SQL-based platforms). 2+ years of experience mentoring or supervising junior data scientists or analysts, including code reviews, training, and structured skill development. 2+ years of experience with Python and SQL programming, using data sources such as SQL Server, Oracle, PostgreSQL, or similar relational databases. 1+ year of experience operationalizing analytics within enterprise governance frameworks, partnering with Data Management, Security, and IT to ensure compliance, reproducibility, and best practices. Education: This classification requires possession of a Master's degree or higher in Data Science, Statistics, Computer Science, or a closely related field. Additional qualifying professional experience may be substituted for the required education on a year-for-year basis. At least one of the following industry-recognized certifications in data science or cloud analytics, such as: • Microsoft Azure Data Scientist Associate (DP-100) • Databricks Certified Data Scientist or Machine Learning Professional • AWS Machine Learning Specialty • Google Professional Data Engineer • or equivalent advanced analytics certifications. The certification is required and may not be substituted with additional experience.

Job Title: Data Scientist V - Data Analytics & Engineering Location: Onsite preferred (Mountain View, CA); Remote considered for strong candidates (US time zones only) Duration: 12 months (possible extension) Required Skills: Strong project or product management experience Excellent communication and consulting skills Proficiency in SQL and Python Nice to Have: Experience with marketing analytics or campaigns Experience in large tech or fast-paced startup environments Familiarity with AI-driven workflows Why Join: High-visibility, cross-functional role Opportunity to work on advanced measurement and automation tools Small, agile team with enterprise-scale impact

About Company, Droisys is an innovation technology company focused on helping companies accelerate their digital initiatives from strategy and planning through execution. We leverage deep technical expertise, Agile methodologies, and data-driven intelligence to modernize systems of engagement and simplify human/tech interaction. Amazing things happen when we work in environments where everyone feels a true sense of belonging and when candidates have the requisite skills and opportunities to succeed. At Droisys, we invest in our talent and support career growth, and we are always on the lookout for amazing talent who can contribute to our growth by delivering top results for our clients. Join us to challenge yourself and accomplish work that matters. Here's the job details, Data Scientist with Gen Ai and Python experience Palo Alto CA- 5 days Onsite Interview Mode:-Phone & F2F Job Overview: Competent Data Scientist, who is independent, results driven and is capable of taking business requirements and building out the technologies to generate statistically sound analysis and production grade ML models DS skills with GenAI and LLM Knowledge, Expertise in Python/Spark and their related libraries and frameworks. Experience in building training ML pipelines and efforts involved in ML Model deployment. Experience in other ML concepts - Real time distributed model inferencing pipeline, Champion/Challenger framework, A/B Testing, Model. Familiar with DS/ML Production implementation. Excellent problem-solving skills, with attention to detail, focus on quality and timely delivery of assigned tasks. Azure cloud and Databricks prior knowledge will be a big plus. Droisys is an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Droisys believes in diversity, inclusion, and belonging, and we are committed to fostering a diverse work environment.

Net2Source is a Global Workforce Solutions Company headquartered at NJ, USA with its branch offices in Asia Pacific Region. We are one of the fastest growing IT Consulting company across the USA and we are hiring " Senior Data Scientist " for one of our clients. We offer a wide gamut of consulting solutions customized to our 450+ clients ranging from Fortune 500/1000 to Start-ups across various verticals like Technology, Financial Services, Healthcare, Life Sciences, Oil & Gas, Energy, Retail, Telecom, Utilities, Technology, Manufacturing, the Internet, and Engineering. Position: Senior Data Scientist Location: Pleasanton, CA (Onsite) - Locals Only Type: Contract Exp Level - 10+ Years Required Skills Design, develop, and deploy advanced marketing models, including: Build and productionize NLP solutions. Partner with Marketing and Business stakeholders to translate business objectives into data science solutions. Work with large-scale structured and unstructured datasets using SQL, Python, and distributed systems. Evaluate and implement state-of-the-art ML/NLP techniques to improve model performance and business impact. Communicate insights, results, and recommendations clearly to both technical and non-technical audiences. Required Qualifications 5+ years of experience in data science or applied machine learning, with a strong focus on marketing analytics. Hands-on experience building predictive marketing models (e.g., segmentation, attribution, personalization). Strong expertise in NLP techniques and libraries (e.g., spa Cy, NLTK, Hugging Face, Gensim). Proficiency in Python, SQL, and common data science libraries (pandas, NumPy, scikit-learn). Solid understanding of statistics, machine learning algorithms, and model evaluation. Experience deploying models into production environments. Strong communication and stakeholder management skills. Why Work With Us? We believe in more than just jobs-we build careers. At Net2Source, we champion leadership at all levels, celebrate diverse perspectives, and empower you to make an impact. Think work-life balance, professional growth, and a collaborative culture where your ideas matter. Our Commitment to Inclusion & Equity Net2Source is an equal opportunity employer, dedicated to fostering a workplace where diverse talents and perspectives are valued. We make all employment decisions based on merit, ensuring a culture of respect, fairness, and opportunity for all, regardless of age, gender, ethnicity, disability, or other protected characteristics. Awards & Recognition America's Most Honored Businesses (Top 10%) Fastest-Growing Staffing Firm by Staffing Industry Analysts INC 5000 List for Eight Consecutive Years Top 100 by Dallas Business Journal Spirit of Alliance Award by Agile1 Maddhuker Singh Sr Account & Delivery Manager ***********************

Meet REVOLVE: REVOLVE is the next-generation fashion retailer for Millennial and Generation Z consumers. As a trusted, premium lifestyle brand, and a go-to online source for discovery and inspiration, we deliver an engaging customer experience from a vast yet curated offering totaling over 45,000 apparel, footwear, accessories and beauty styles. Our dynamic platform connects a deeply engaged community of millions of consumers, thousands of global fashion influencers, and more than 500 emerging, established and owned brands. Through 16 years of continued investment in technology, data analytics, and innovative marketing and merchandising strategies, we have built a powerful platform and brand that we believe is connecting with the next generation of consumers and is redefining fashion retail for the 21st century. For more information please visit **************** At REVOLVE the most successful team members have a thirst and the creativity to make this the top e-commerce brand in the world. With a team of 1,000+ based out of Cerritos, California we are a dynamic bunch that are motivated by getting the company to the next level. It's our goal to hire high-energy, diverse, bright, creative, and flexible individuals who thrive in a fast-paced work environment. In return, we promise to keep REVOLVE a company where inspired people will always thrive. To take a behind the scenes look at the REVOLVE “corporate” lifestyle check out our Instagram @REVOLVEcareers or #lifeatrevolve. Are you ready to set the standard for Premium apparel? Main purpose of the Senior Data Science Analyst role: Use a diverse skill sets across math and computer science, dedicated to solving complex and analytically challenging problems here at Revolve. Major Responsibilities: Essential Duties and Responsibilities include the following. Other duties may be assigned. Partner closely with business leaders in Marketing, Product, Operations, Buying team to plan out valuable data science projects Conduct complex analysis and build models to uncover key learning form data, leading to appropriate strategy recommendations. Work closely with the DBA to improve BI's infrastructure, architect the reporting system, and invest in time for technical proof of concept. Work closely with the business intelligence and tech team to define, automate and validate the extraction of new metrics from various data sources for use in future analysis Work alongside business stakeholders to apply our findings and models in website personalization, product recommendations, marketing optimization, to fraud detection, demand forecast, CLV prediction. Required Competencies: To perform the job successfully, an individual should demonstrate the following competencies: Outstanding analytical skills, with strong academic background in statistics, math, science or technology. High comfort level with programming, ability to learn and adopt new technology with short turn-around time. Knowledge of quantitative methods in statistics and machine learning Intense intellectual curiosity - strong desire to always be learning Proven business acumen and results oriented. Ability to demonstrate logical thinking and problem solving skills Strong attention to detail Minimum Qualifications: Master Degree is required 3+ years of DS and ML experience in a strong analytical environment. Proficient in Python, NumPy and other packages Familiar with statistical and ML methodology: causal inference, logistic regression, tree-based models, clustering, model validation and interpretations. Experience with AB Testing and pseudo-A/B test setup and evaluations Advanced SQL experience, query optimization, data extract Ability to build, validate, and productionize models Preferred Qualifications: Strong business acumen Experience in deploying end to end Machine Learning models 5+ years of DS and ML experience preferred Advanced SQL and Python, with query and coding optimization experience Experience with E-commerce marketing and product analytics is a plus A successful candidate works well in a dynamic environment with minimal supervision. At REVOLVE we all roll up our sleeves to pitch-in and do whatever it takes to get the job done. Each day is a little different, it's what keeps us on our toes and excited to come to work every day. A reasonable estimate of the current base salary range is $120,000 to $150,000 per year.

🌎 San Francisco (Hybrid) 💼 Founding/Staff Data Engineer 💵 $200-300k base Our client is an elite applied AI research and product lab building AI-native systems for finance-and pushing frontier models into real production environments. Their work sits at the intersection of data, research, and high-stakes financial decision-making. As the Founding Data Engineer, you will own the data platform that powers everything: models, experiments, and user-facing products relied on by demanding financial customers. You'll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one. What you'll do: Design and build the core data platform, ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers to ship production-grade data and feature pipelines that power cutting-edge models. Establish data quality, observability, lineage, and reproducibility across both experimentation and production workloads. Deploy and operate data services using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make foundational choices on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems-rewriting pipelines or infrastructure when it's the right long-term decision. Ideal candidate: Have owned or built high-performance data systems end-to-end, directly supporting production applications and ML models. Are strongest in backend and data infrastructure, with enough frontend literacy to integrate cleanly with web products when needed. Can design and evolve backend services and pipelines (Node.js or Python) to support new product features and research workflows. Are an expert in at least one statically typed language, with a strong bias toward type safety, correctness, and maintainable systems. Have deployed data workloads and services using Docker and Kubernetes on a major cloud provider. Are comfortable making hard calls-simplifying, refactoring, or rebuilding legacy pipelines when quality and scalability demand it. Use AI tools to accelerate your work, but rigorously review and validate AI-generated code, insisting on sound system design. Thrive in a high-bar, high-ownership environment with other exceptional engineers. Love deep technical problems in data infrastructure, distributed systems, and performance. Nice to have: Experience working with financial data (market, risk, portfolio, transactional, or alternative datasets). Familiarity with ML infrastructure, such as feature stores, experiment tracking, or model serving systems. Background in a high-growth startup or a foundational infrastructure role. Compensation & setup: Competitive salary and founder-level equity Hybrid role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact

If you're hands on with modern data platforms, cloud tech, and big data tools and you like building solutions that are secure, repeatable, and fast, this role is for you. As a Senior Data Engineer, you will design, build, and maintain scalable data pipelines that transform raw information into actionable insights. The ideal candidate will have strong experience across modern data platforms, cloud environments, and big data technologies, with a focus on building secure, repeatable, and high-performing solutions. Responsibilities: Design, develop, and maintain secure, scalable data pipelines to ingest, transform, and deliver curated data into the Common Data Platform (CDP). Participate in Agile rituals and contribute to delivery within the Scaled Agile Framework (SAFe). Ensure quality and reliability of data products through automation, monitoring, and proactive issue resolution. Deploy alerting and auto-remediation for pipelines and data stores to maximize system availability. Apply a security first and automation-driven approach to all data engineering practices. Collaborate with cross-functional teams (data scientists, analysts, product managers, and business stakeholders) to align infrastructure with evolving data needs. Stay current on industry trends and emerging tools, recommending improvements to strengthen efficiency and scalability. Qualifications: Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent experience). At least 3 years of experience with Python and PySpark, including Jupyter notebooks and unit testing. At least 2 years of experience with Databricks, Collibra, and Starburst. Proven work with relational and NoSQL databases, including STAR and dimensional modeling approaches. Hands-on experience with modern data stacks: object stores (S3), Spark, Airflow, lakehouse architectures, and cloud warehouses (Snowflake, Redshift). Strong background in ETL and big data engineering (on-prem and cloud). Work within enterprise cloud platforms (CFS2, Cloud Foundational Services 2/EDS) for governance and compliance. Experience building end-to-end pipelines for structured, semi-structured, and unstructured data using Spark.

We're the data team behind Midjourney's image generation models. We handle the dataset side: processing, filtering, scoring, captioning, and all the distributed compute that makes high-quality training data possible. What you'd be working on: Large-scale dataset processing and filtering pipelines Training classifiers for content moderation and quality assessment Models for data quality and aesthetic evaluation Data visualization tools for experimenting on dataset samples Testing/simulating distributed inference pipelines Monitoring dashboards for data quality and pipeline health Performance optimization and infrastructure scaling Occasionally jumping into inference optimization and other cross-team projects Our current stack: PySpark, Slurm, distributed batch processing across hybrid cloud setup. We're pragmatic about tools - if there's something better, we'll switch. We're looking for someone strong in either: Data engineering/ML pipelines at scale, or Cloud/infrastructure with distributed systems experience Don't need exact tech matches - comfort with adjacent technologies and willingness to learn matters more. We work with our own hardware plus GCP and other providers, so adaptability across different environments is valuable. Location: SF office a few times per week (we may make exceptions on location for truly exceptional candidates) The role offers variety, our team members often get pulled into different projects across the company, from dataset work to inference optimization. If you're interested in the intersection of large-scale data processing and cutting-edge generative AI, we'd love to hear from you.

We are seeking a Data Analytics Engineer to join our team who serves as a hybrid Database Administrator, Data Engineer, and Data Analyst, responsible for managing core data infrastructure, developing and maintaining ETL pipelines, and delivering high-quality analytics and visual insights to executive stakeholders. This role bridges technical execution with business intelligence, ensuring that data across Salesforce, financial, and operational systems is accurate, accessible, and strategically presented. Essential Functions Database Administration: Oversee and maintain database servers, ensuring performance, reliability, and security. Manage user access, backups, and data recovery processes while optimizing queries and database operations. Data Engineering (ELT): Design, build, and maintain robust ELT pipelines (SQL/DBT or equivalent) to extract, transform, and load data across Salesforce, financial, and operational sources. Ensure data lineage, integrity, and governance throughout all workflows. Data Modeling & Governance: Design scalable data models and maintain a governed semantic layer and KPI catalog aligned with business objectives. Define data quality checks, SLAs, and lineage standards to reconcile analytics with finance source-of-truth systems. Analytics & Reporting: Develop and manage executive-facing Tableau dashboards and visualizations covering key lending and operational metrics - including pipeline conversion, production, credit quality, delinquency/charge-offs, DSCR, and LTV distributions. Presentation & Insights: Translate complex datasets into clear, compelling stories and presentations for leadership and cross-functional teams. Communicate findings through visual reports and executive summaries to drive strategic decisions. Collaboration & Integration: Partner with Finance, Capital Markets, and Operations to refine KPIs and perform ad-hoc analyses. Collaborate with Engineering to align analytical and operational data, manage integrations, and support system scalability. Enablement & Training: Conduct training sessions, create documentation, and host data office hours to promote data literacy and empower business users across the organization. Competencies & Skills Advanced SQL proficiency with strong data modeling, query optimization, and database administration experience (PostgreSQL, MySQL, or equivalent). Hands-on experience managing and maintaining database servers and optimizing performance. Proficiency with ETL/ELT frameworks (DBT, Airflow, or similar) and cloud data stacks (AWS/Azure/GCP). Strong Tableau skills - parameters, LODs, row-level security, executive-level dashboard design, and storytelling through data. Experience with Salesforce data structures and ingestion methods. Proven ability to communicate and present technical data insights to executive and non-technical stakeholders. Solid understanding of lending/financial analytics (pipeline conversion, delinquency, DSCR, LTV). Working knowledge of Python for analytics tasks, cohort analysis, and variance reporting. Familiarity with version control (Git), CI/CD for analytics, and data governance frameworks. Excellent organizational, documentation, and communication skills with a strong sense of ownership and follow-through. Education & Experience Bachelor's degree in Computer Science, Engineering, Information Technology, Data Analytics, or a related field. 3+ years of experience in data analytics, data engineering, or database administration roles. Experience supporting executive-level reporting and maintaining database infrastructure in a fast-paced environment.

Thank you for stopping by to take a look at the Data Integration Engineer role I posted here on LinkedIN, I appreciate it. If you have read my s in the past, you will recognize how I write job descriptions. If you are new, allow me to introduce myself. My name is Tom Welke. I am Partner & VP at RSM Solutions, Inc and I have been recruiting technical talent for more than 23 years and been in the tech space since the 1990s. Due to this, I actually write JD's myself...no AI, no 'bots', just a real live human. I realized a while back that looking for work is about as fun as a root canal with no anesthesia...especially now. So, rather than saying 'must work well with others' and 'team mindset', I do away with that kind of nonsense and just tell it like it is. So, as with every role I work on, social fit is almost as important as technical fit. For this one, technical fit is very very important. But, we also have some social fit characteristics that are important. This is the kind of place that requires people to dive in and learn. The hiring manager for this one is actually a very dear friend of mine. He said something interesting to me not all that long ago. He mentioned, if you aren't spending at least an hour a day learning something new, you really are doing yourself a disservice. This is that classic environment where no one says 'this is not my job'. So that ability to jump in and help is needed for success in this role. This role is being done onsite in Irvine, California. I prefer working with candidates that are already local to the area. If you need to relocate, that is fine, but there are no relocation dollars available. I can only work with US Citizens or Green Card Holders for this role. I cannot work with H1, OPT, EAD, F1, H4, or anyone that is not already a US Citizen or Green Card Holder for this role. The Data Engineer role is similar to the Data Integration role I posted. However, this one is mor Ops focused, with the orchestration of deployment and ML flow, and including orchestrating and using data on the clusters and managing how the models are performing. This role focuses on coding & configuring on the ML side of the house. You will be designing, automating, and observing end to end data pipelines that feed this client's Kubeflow driven machine learning platform, ensuring models are trained, deployed, and monitored on trustworthy, well governed data. You will build batch/stream workflows, wire them into Azure DevOps CI/CD, and surface real time health metrics in Prometheus + Grafana dashboards to guarantee data availability. The role bridges Data Engineering and MLOps, allowing data scientists to focus on experimentation and the business sees rapid, reliable predictive insight. Here are some of the main responsibilities: Design and implement batch and streaming pipelines in Apache Spark running on Kubernetes and Kubeflow Pipelines to hydrate feature stores and training datasets. Build high throughput ETL/ELT jobs with SSIS, SSAS, and T SQL against MS SQL Server, applying Data Vault style modeling patterns for auditability. Integrate source control, build, and release automation using GitHub Actions and Azure DevOps for every pipeline component. Instrument pipelines with Prometheus exporters and visualize SLA, latency, and error budget metrics to enable proactive alerting. Create automated data quality and schema drift checks; surface anomalies to support a rapid incident response process. Use MLflow Tracking and Model Registry to version artifacts, parameters, and metrics for reproducible experiments and safe rollbacks. Work with data scientists to automate model retraining and deployment triggers within Kubeflow based on data freshness or concept drift signals. Develop PowerShell and .NET utilities to orchestrate job dependencies, manage secrets, and publish telemetry to Azure Monitor. Optimize Spark and SQL workloads through indexing, partitioning, and cluster sizing strategies, benchmarking performance in CI pipelines. Document lineage, ownership, and retention policies; ensure pipelines conform to PCI/SOX and internal data governance standards. Here is what we are seeking: At least 6 years of experience building data pipelines in Spark or equivalent. At least 2 years deploying workloads on Kubernetes/Kubeflow. At least 2 years of experience with MLflow or similar experiment‑tracking tools. At least 6 years of experience in T‑SQL, Python/Scala for Spark. At least 6 years of PowerShell/.NET scripting. At least 6 years of experience with with GitHub, Azure DevOps, Prometheus, Grafana, and SSIS/SSAS. Kubernetes CKA/CKAD, Azure Data Engineer (DP‑203), or MLOps‑focused certifications (e.g., Kubeflow or MLflow) would be great to see. Mentor engineers on best practices in containerized data engineering and MLOps.

Hybrid role 3X a week in office in Elk Grove, CA; no remote capabilities This is a direct hire opportunity. We're seeking a seasoned Senior Data Platform Engineer to design, build, and optimize scalable data solutions that power analytics, reporting, and AI/ML initiatives. This full‑time role is hands‑on, working with architects, analysts, and business stakeholders to ensure data systems are reliable, secure, and high‑performing. Responsibilites: Build and maintain robust data pipelines (structured, semi‑structured, unstructured). Implement ETL workflows with Spark, Delta Lake, and cloud‑native tools. Support big data platforms (Databricks, Snowflake, GCP) in production. Troubleshoot and optimize SQL queries, Spark jobs, and workloads. Ensure governance, security, and compliance across data systems. Integrate workflows into CI/CD pipelines with Git, Jenkins, Terraform. Collaborate cross‑functionally to translate business needs into technical solutions. Qualifications: 7+ years in data engineering with production pipeline experience. Expertise in Spark ecosystem, Databricks, Snowflake, GCP. Strong skills in PySpark, Python, SQL. Experience with RAG systems, semantic search, and LLM integration. Familiarity with Kafka, Pub/Sub, vector databases. Proven ability to optimize ETL jobs and troubleshoot production issues. Agile team experience and excellent communication skills. Certifications in Databricks, Snowflake, GCP, or Azure. Exposure to Airflow, BI tools (Power BI, Looker Studio).

Staff Data Scientist | San Francisco | $250K-$300K + Equity We're partnering with one of the fastest-growing AI companies in the world to hire a Staff Data Scientist. Backed by over $230M from top-tier investors and already valued at over $1B, they've secured customers that include some of the most recognizable names in tech. Their AI platform powers millions of daily interactions and is quickly becoming the enterprise standard for conversational AI. In this role, you'll bring rigorous analytics and experimentation leadership that directly shapes product strategy and company performance. What you'll do: Drive deep-dive analyses on user behavior, product performance, and growth drivers Design and interpret A/B tests to measure product impact at scale Build scalable data models, pipelines, and dashboards for company-wide use Partner with Product and Engineering to embed experimentation best practices Evaluate ML models, ensuring business relevance, performance, and trade-off clarity What we're looking for: 5+ years in data science or product analytics at scale (consumer or marketplace preferred) Advanced SQL and Python skills, with strong foundations in statistics and experimental design Proven record of designing, running, and analyzing large-scale experiments Ability to analyze and reason about ML models (classification, recommendation, LLMs) Strong communicator with a track record of influencing cross-functional teams If you're excited by the sound of this challenge- apply today and we'll be in touch.

We're hiring a Senior/Lead Data Engineer to join a fast-growing AI startup. The team comes from a billion dollar AI company, and has raised a $40M+ seed round. You'll need to be comfortable transforming and moving data in a new 'group level' data warehouse, from legacy sources. You'll have a strong data modeling background. Proven proficiency in modern data transformation tools, specifically dbt and/or SQLMesh. Exceptional ability to apply systems thinking and complex problem-solving to ambiguous challenges. Experience within a high-growth startup environment is highly valued. Deep, practical knowledge of the entire data lifecycle, from generation and governance through to advanced downstream applications (e.g., fueling AI/ML models, LLM consumption, and core product features). Outstanding ability to communicate technical complexity clearly, synthesizing information into actionable frameworks for executive and cross-functional teams.

Midjourney is a research lab exploring new mediums to expand the imaginative powers of the human species. We are a small, self-funded team focused on design, human infrastructure, and AI. We have no investors, no big company controlling us, and no advertisers. We are 100% supported by our amazing community. Our tools are already used by millions of people to dream, to explore, and to create. But this is just the start. We think the story of the 2020s is about building the tools that will remake the world for the next century. We're making those tools, to expand what it means to be human. Core Responsibilities: Design and maintain data pipelines to consolidate information across multiple sources (subscription platforms, payment systems, infrastructure and usage monitoring, and financial systems) into a unified analytics environment Build and manage interactive dashboards and self-service BI tools that enable leadership to track key business metrics including revenue performance, infrastructure costs, customer retention, and operational efficiency Serve as technical owner of our financial planning platform (Pigment or similar), leading implementation and build-out of models, data connections, and workflows in partnership with Finance leadership to translate business requirements into functional system architecture Develop automated data quality checks and cleaning processes to ensure accuracy and consistency across financial and operational datasets Partner with Finance, Product and Operations teams to translate business questions into analytical frameworks, including cohort analysis, cost modeling, and performance trending Create and maintain documentation for data models, ETL processes, dashboard logic, and system workflows to ensure knowledge continuity Support strategic planning initiatives by building financial models, scenario analyses, and data-driven recommendations for resource allocation and growth investments Required Qualifications: 3-5+ years experience in data engineering, analytics engineering, or similar role with demonstrated ability to work with large-scale datasets Strong SQL skills and experience with modern data warehousing solutions (BigQuery, Snowflake, Redshift, etc.) Proficiency in at least one programming language (Python, R) for data manipulation and analysis Experience with BI/visualization tools (Looker, Tableau, Power BI, or similar) Hands-on experience administering enterprise financial systems (NetSuite, SAP, Oracle, or similar ERP platforms) Experience working with Stripe Billing or similar subscription management platforms, including data extraction and revenue reporting Ability to communicate technical concepts clearly to non-technical stakeholders

If you're hands on with modern data platforms, cloud tech, and big data tools and you like building solutions that are secure, repeatable, and fast, this role is for you. As a Senior Data Engineer, you will design, build, and maintain scalable data pipelines that transform raw information into actionable insights. The ideal candidate will have strong experience across modern data platforms, cloud environments, and big data technologies, with a focus on building secure, repeatable, and high-performing solutions. Responsibilities: Design, develop, and maintain secure, scalable data pipelines to ingest, transform, and deliver curated data into the Common Data Platform (CDP). Participate in Agile rituals and contribute to delivery within the Scaled Agile Framework (SAFe). Ensure quality and reliability of data products through automation, monitoring, and proactive issue resolution. Deploy alerting and auto-remediation for pipelines and data stores to maximize system availability. Apply a security first and automation-driven approach to all data engineering practices. Collaborate with cross-functional teams (data scientists, analysts, product managers, and business stakeholders) to align infrastructure with evolving data needs. Stay current on industry trends and emerging tools, recommending improvements to strengthen efficiency and scalability. Qualifications: Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent experience). At least 3 years of experience with Python and PySpark, including Jupyter notebooks and unit testing. At least 2 years of experience with Databricks, Collibra, and Starburst. Proven work with relational and NoSQL databases, including STAR and dimensional modeling approaches. Hands-on experience with modern data stacks: object stores (S3), Spark, Airflow, lakehouse architectures, and cloud warehouses (Snowflake, Redshift). Strong background in ETL and big data engineering (on-prem and cloud). Work within enterprise cloud platforms (CFS2, Cloud Foundational Services 2/EDS) for governance and compliance. Experience building end-to-end pipelines for structured, semi-structured, and unstructured data using Spark.