Senior data scientist jobs in Cathedral City, CA - 2,151 jobs

T3W Business Solutions, Inc. is a Woman-Owned Small Business with Headquarters located in San Diego, CA. It is our mission to help our clients develop strategies to optimize their use of space and resources resulting in maximum benefits; we also deliver quality data and analysis to support our client's daily facility operations, planning, and compliance programs. We are looking for a Sr. Data Scientist in San Diego, California. **Contingent Upon Contract Award** Summary Builds advanced analytics, machine learning models, forecasting tools, and data products to support FRCSW strategic and operational decisions. Analyzes large structured/unstructured datasets, constructs pipelines, and develops dashboards visualizing key performance indicators. Leads data standardization, modeling, statistical analysis, and automation initiatives. Guides team members on analytic methods and ensures alignment with enterprise data strategy. Responsibilities Apply statistical modeling, machine learning, and data visualization techniques. Develop predictive models and dashboards using Power BI, Qlik, or Tableau. Analyze large structured and unstructured datasets. Collaborate with IT, program management, and financial teams to support data-driven decisions. Requirements Bachelor's degree in Data Science, Statistics, or a related field. 10+ years of professional data analytics experience. Proficiency in Python, R, SQL, and visualization tools. Must possess an active Secret Clearance - Required This contractor and subcontractor shall abide by the requirements of 41 CFR §§ 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. Moreover, these regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability.

Salary: $200-250k base + RSUs This fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We're looking for a Staff Data Scientist to drive Sales and Go-to-Market (GTM) analytics, applying advanced modeling and experimentation to accelerate revenue growth and optimize the full sales funnel. About the Role As the senior data scientist supporting Sales and GTM, you will combine statistical modeling, experimentation, and advanced analytics to inform strategy and guide decision-making across our revenue organization. Your work will help leadership understand pipeline health, predict outcomes, and identify the levers that unlock sustainable growth. Key Responsibilities Model the Business: Build forecasting and propensity models for pipeline generation, conversion rates, and revenue projections. Optimize the Sales Funnel: Analyze lead scoring, opportunity progression, and deal velocity to recommend improvements in acquisition, qualification, and close rates. Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of pricing, incentives, and campaign initiatives. Advanced Analytics for GTM: Apply machine learning and statistical techniques to segment accounts, predict churn/expansion, and identify high-value prospects. Cross-Functional Partnership: Work closely with Sales, Marketing, RevOps, and Product to influence GTM strategy and ensure data-driven decisions. Data Infrastructure Collaboration: Partner with Analytics Engineering to define data requirements, ensure data quality, and enable self-serve reporting. Strategic Insights: Present findings to executive leadership, translating complex analyses into actionable recommendations. About You Experience: 6+ years in data science or advanced analytics roles, with significant time spent in B2B SaaS or developer tools environments. Technical Depth: Expert in SQL and proficient in Python or R for statistical modeling, forecasting, and machine learning. Domain Knowledge: Strong understanding of sales analytics, revenue operations, and product-led growth (PLG) motions. Analytical Rigor: Skilled in experimentation design, causal inference, and building predictive models that influence GTM strategy. Communication: Exceptional ability to tell a clear story with data and influence senior stakeholders across technical and business teams. Business Impact: Proven record of driving measurable improvements in pipeline efficiency, conversion rates, or revenue outcomes.

Are you an experienced Data Scientist with a desire to excel? If so, then Talent Software Services may have the job for you! Our client is seeking an experienced Data Scientist to work at their company in Novato, CA. Client's Data Science is responsible for designing, capturing, analyzing, and presenting data that can drive key decisions for Clinical Development, Medical Affairs, and other business areas of Client. With a quality-by-design culture, Data Science builds quality data that is fit-for-purpose to support statistically sound investigation of critical scientific questions. The Data Science team develops solid analytics that are visually relevant and impactful in supporting key data-driven decisions across Client. The Data Management Science (DMS) group contributes to Data Science by providing complete, correct, and consistent analyzable data at data, data structure and documentation levels following international standards and GCP. The DMS Center of Risk Based Quality Management (RBQM) sub-function is responsible for the implementation of a comprehensive, cross-functional strategy to proactively manage quality risks for clinical trials. Starting at protocol development, the team collaborates to define critical-to-quality factors, design fit-for-purpose quality strategies, and enable ongoing oversight through centralized monitoring and data-driven risk management. The RBQM Data Scientist supports central monitoring and risk-based quality management (RBQM) for clinical trials. This role focuses on implementing and running pre-defined KRIs, QTLs, and other risk metrics using clinical data, with strong emphasis on SAS programming to deliver robust and scalable analytics across multiple studies. Primary Responsibilities/Accountabilities: The RBQM Data Scientist may perform a range of the following responsibilities, depending upon the study's complexity and the study's development stage: Implement and maintain pre-defined KRIs, QTLs, and triggers using robust SAS programs/macros across multiple clinical studies. Extract, transform, and integrate data from EDC systems (e.g., RAVE) and other clinical sources into analysis-ready SAS datasets. Run routine and ad-hoc RBQM/central monitoring outputs (tables, listings, data extracts, dashboard feeds) to support signal detection and study review. Perform QC and troubleshooting of SAS code; ensure outputs are accurate and efficient. Maintain clear technical documentation (specifications, validation records, change logs) for all RBQM programs and processes. Collaborate with Central Monitors, Central Statistical Monitors, Data Management, Biostatistics, and Study Operations to understand requirements and ensure correct implementation of RBQM metrics. Qualifications: PhD, MS, or BA/BS in statistics, biostatistics, computer science, data science, life science, or a related field. Relevant clinical development experience (programming, RBM/RBQM, Data Management), for example: PhD: 3+ years MS: 5+ years BA/BS: 8+ years Advanced SAS programming skills (hard requirement) in a clinical trials environment (Base SAS, Macro, SAS SQL; experience with large, complex clinical datasets). Hands-on experience working with clinical trial data.•Proficiency with Microsoft Word, Excel, and PowerPoint. Technical - Preferred / Strong Plus Experience with RAVE EDC. Awareness or working knowledge of CDISC, CDASH, SDTM standards. Exposure to R, Python, or JavaScript and/or clinical data visualization tools/platforms. Preferred: Knowledge of GCP, ICH, FDA guidance related to clinical trials and risk-based monitoring. Strong analytical and problem-solving skills; ability to interpret complex data and risk outputs. Effective communication and teamwork skills; comfortable collaborating with cross-functional, global teams. Ability to manage multiple programming tasks and deliver high-quality work in a fast-paced environment.

About GoodLeap GoodLeap is a technology company delivering best-in-class financing and software products for sustainable solutions, from solar panels and batteries to energy-efficient HVAC, heat pumps, roofing, windows, and more. Over 1 million homeowners have benefited from our simple, fast, and frictionless technology that makes the adoption of these products more affordable, accessible, and easier to understand. Thousands of professionals deploying home efficiency and solar solutions rely on GoodLeap's proprietary, AI-powered applications and developer tools to drive more transparent customer communication, deeper business intelligence, and streamlined payment and operations. Our platform has led to more than $30 billion in financing for sustainable solutions since 2018. GoodLeap is also proud to support our award-winning nonprofit, GivePower, which is building and deploying life-saving water and clean electricity systems, changing the lives of more than 1.6 million people across Africa, Asia, and South America. Position Summary The GoodLeap team is looking for a hands‑on Data Engineer with a strong background in API data integrations, Spark processing and data lake development. The focus of this role will be on ingesting production energy data and helping get the aggregated metrics to the many teams in GoodLeap that need them. The successful candidate is a highly motivated individual with strong technical skills to create secure and performant data pipelines as well as support our foundational enterprise data warehouse. The ideal candidate is passionate about quality and has a bold, visionary approach to data practices in a modern finance enterprise. The candidate in this role will be required to work closely with cross‑functional teams to effectively coordinate the complex interdependencies inherent in the applications. Typical teams we collaborate with are Analytics & Reporting, Origination Platform engineers and AI developers. We are looking for a hardworking and passionate engineer who wants to make a difference with the tools they develop. Essential Job Duties and Responsibilities Implement data integrations across the organization as well as with business applications Develop and maintain data oriented web applications with scalable web services Participate in the design and development of projects, either independently or in a team Utilize agile software development lifecycle and DevOps principles Be the data stewards of the organization upholding quality and availability standards for our downstream consumers Be self‑sufficient and fully own the responsibility of executing projects from inception to delivery Provide mentorship to team members including pair programming and skills development Participate in data design and architecture discussions, considering solutions in the context of the larger GoodLeap ecosystem Required Skills, Knowledge & Abilities 6-10 years of full‑time Data Analysis and/or Software Development experience Experience with an end to end reporting & analytics technology: data warehousing (SQL, NoSQL) to BI/Visualization (Tableau, PowerBI, Excel) Degree in Computer Science or related discipline Experience with DataBricks/Spark processing Expertise with relational databases (including functional SQL/stored procedures) and non‑relational databases (MongoDB, DynamoDB, Elastic Search) Experience with orchestrating data pipelines with modern tools such as Airflow Strong knowledge and hands‑on experience with open source web frameworks (e.g. Vue /React) Solid understanding of performance implications and scalability of code Experience with Amazon Web Services (IAM, Cognito, EC2, S3, RDS, Cloud Formation) Experience with messaging paradigms and serverless technologies (Lambda, SQS, SNS, SES) Experience working with server‑less applications on public clouds (e.g. AWS) Experience with large, complex codebases and know how to maintain them $160,000 - $210,000 a year In addition to the above salary, this role may be eligible for a bonus and equity. Additional Information Regarding Job Duties and s Job duties include additional responsibilities as assigned by one's supervisor or other managers related to the position/department. This job description is meant to describe the general nature and level of work being performed; it is not intended to be construed as an exhaustive list of all responsibilities, duties and other skills required for the position. The Company reserves the right at any time with or without notice to alter or change job responsibilities, reassign or transfer job position or assign additional job responsibilities, subject to applicable law. The Company shall provide reasonable accommodations of known disabilities to enable a qualified applicant or employee to apply for employment, perform the essential functions of the job, or enjoy the benefits and privileges of employment as required by the law. If you are an extraordinary professional who thrives in a collaborative work culture and values a rewarding career, then we want to work with you! Apply today! We are committed to protecting your privacy. To learn more about how we collect, use, and safeguard your personal information during the application process, please review our Employment Privacy Policy and Recruiting Policy on AI. #J-18808-Ljbffr

A leading security technology firm in California is seeking a skilled Data Science Engineer. You will harness the power of unsupervised machine learning to detect fraudulent activities across various sectors. Ideal candidates have experience with Java/C++, data structures, and machine learning. The company offers competitive pay, flexible schedules, equity participation, health benefits, a collaborative environment, and unique perks such as catered lunches and game nights. #J-18808-Ljbffr

Mechanical design, the work done in CAD, is the rate-limiter for progress in the physical world. However, there are only 2-4 million people on Earth who know how to CAD. But what if hundreds of millions could? What if creating something in the real world were as easy as imagining the use case, or sketching it on paper? Backflip is building a foundation model for mechanical design: unifying the world's scattered engineering knowledge into an intelligent, end-to-end design environment. Our goal is to enable anyone to imagine a solution and hit “print.” Founded by a second-time CEO in the same space (first company: Markforged), Backflip combines deep industry insight with breakthrough AI research. Backed by a16z and NEA, we raised a $30M Series A and built a deeply technical, mission-driven team. We're building the AI foundation that tomorrow's space elevators, nanobots, and spaceships will be built in. If you're excited to define the next generation of hard tech, come build it with us. The Role We're looking for a Staff Machine Learning Data Engineer to lead and build the data pipelines powering Backflip's foundation model for manufacturing and CAD. You'll design the systems, tools, and strategies that turn the world's engineering knowledge - text, geometry, and design intent - into high-quality training data. This is a core leadership role within the AI team, driving the data architecture, augmentation, and evaluation that underpin our model's performance and evolution. You'll collaborate with Machine Learning Engineers to run data-driven experiments, analyze results, and deliver AI products that shape the future of the physical world. What You'll Do Architect and own Backflip's ML data pipeline, from ingestion to processing to evaluation. Define data strategy: establish best practices for data augmentation, filtering, and sampling at scale. Design scalable data systems for multimodal training (text, geometry, CAD, and more). Develop and automate data collection, curation, and validation workflows. Collaborate with MLEs to design and execute experiments that measure and improve model performance. Build tools and metrics for dataset analysis, monitoring, and quality assurance. Contribute to model development through insights grounded in data, shaping what, how, and when we train. Who You Are You've built and maintained ML data pipelines at scale, ideally for foundation or generative models, that shipped into production in the real world. You have deep experience with data engineering for ML, including distributed systems, data extraction, transformation, and loading, and large-scale data processing (e.g. PySpark, Beam, Ray, or similar). You're fluent in Python and experienced with ML frameworks and data formats (Parquet, TFRecord, HuggingFace datasets, etc.). You've developed data augmentation, sampling, or curation strategies that improved model performance. You think like both an engineer and an experimentalist: curious, analytical, and grounded in evidence. You collaborate well across AI development, infra, and product, and enjoy building the data systems that make great models possible. You care deeply about data quality, reproducibility, and scalability. You're excited to help shape the future of AI for physical design. Bonus points if: You are comfortable working with a variety of complex data formats, e.g. for 3D geometry kernels or rendering engines. You have an interest in math, geometry, topology, rendering, or computational geometry. You've worked in 3D printing, CAD, or computer graphics domains. Why Backflip This is a rare opportunity to own the data backbone of a frontier foundation model, and help define how AI learns to design the physical world. You'll join a world-class, mission-driven team operating at the intersection of research, engineering, and deep product sense, building systems that let people design the physical world as easily as they imagine it. Your work will directly shape the performance, capability, and impact of Backflip's foundation model, the core of how the world will build in the future. Let's build the tools the future will be made in. #J-18808-Ljbffr

A leading AI technology company based in Palo Alto, CA is seeking a Machine Learning Data Engineer. You will build and optimize the data infrastructure for our machine learning systems while collaborating with ML engineers and infrastructure teams. The ideal candidate has a strong engineering background in Python, experience in production data pipelines, and a deep understanding of distributed systems. This role offers comprehensive benefits, a collaborative environment, and opportunities for innovative contributions. #J-18808-Ljbffr

A pioneering audio tech company based in San Francisco is searching for a Founding Machine Learning Infrastructure Engineer. In this role, you will build and scale the core infrastructure that powers cutting-edge audio ML products. You will lead the development of systems for training and deploying models. Candidates should have over 5 years of backend experience with strong skills in cloud infrastructure and machine learning principles. The company offers benefits like unlimited PTO and comprehensive health coverage. #J-18808-Ljbffr

About us At Eon, we are at the forefront of large-scale neuroscientific data collection. Our mission is to enable the safe and scalable development of brain emulation technology to empower humanity over the next decade, beginning with the creation of a fully emulated digital twin of a mouse. Role We're a San Francisco team collecting very large microscopy datasets and we need an expert to design and implement our end-to-end data pipeline, from high-rate ingest to multi-petabyte storage and downstream processing. You'll own the strategy (on-prem vs. S3 or hybrid), the bill of materials, and the deployment, and you'll be on the floor wiring, racking, tuning, and validating performance. Our current instruments generate data at ~1+ GB/s sustained (higher during bursts) and the program will accumulate multiple petabyes total over time. You'll help us choose and implement the right architecture considering reliability and cost controls. Outcomes (what success looks like) Within 2 weeks: Implement an immediate data-handling strategy that reliably ingests our initial data streams. Within 2 weeks: Deliver a documented medium-term data architecture covering storage, networking, ingest, and durability. Within 1 month: Operationalize the medium-term pipeline in production (ingest → buffer → long-term store → compute access). Ongoing: Maintain ≥95% uptime for the end-to-end data-handling pipeline after setup. Responsibilities Architect ingest & storage: Choose and implement an on-prem hardware and data pipeline design or a cloud/S3 alternative with explicit cost and performance tradeoffs at multi-petabyte scale. Set up a sustained-write ingest path ≥1 GB/s with adequate burst headroom (camera/frame-to-disk), including networking considerations, cooling, and throttling safeguards. Optimize footprint & cost: Incorporate on-the-fly compression/downsampling options and quantify CPU budget vs. write-speed tradeoffs; document when/where to compress to control $/PB. Integrate with acquisition workflows ensuring image data and metadata are compatible with downstream stitching/flat-field correction pipelines. Enable downstream compute: Expose the data to segmentation/analysis stacks (local GPU nodes or cloud). Skills 5+ years designing and deploying high-throughput storage or HPC pipelines (≥1 GB/s sustained ingest) in production. Deep hands-on with: NVMe RAID/striping, ZFS/MDRAID/erasure coding, PCIe topology, NUMA pinning, Linux performance tuning, and NIC offload features. Proven delivery of multi-GB/s ingest systems and petabyte-scale storage in production (life-sciences, vision, HPC, or media). Experience building tiered storage systems (NVMe → HDD/object) and validating real-world throughput under sustained load. Practical S3/object-storage know-how (AWS S3 and/or on-prem S3-compatible systems) with lifecycle, versioning, and cost controls. Data integrity & reliability: snapshots, scrubs, replication, erasure coding, and backup/DR for PB-scale systems. Networking: ****25/40/100 GbE (SFP+/SFP28), RDMA/ RoCE/iWARP familiarity; switch config and path tuning. Ability to spec and rack hardware: selecting chassis/backplanes, RAID/HBA cards, NICs, and cooling strategies to prevent NVMe throttling under sustained writes. Ideal skills: Experience with microscopy or scientific imaging ingest at frame-to-disk speeds, including Micro-Manager-based pipelines and raw-to-containerized format conversions. Experience with life science imaging data a plus. Engagement details Contract (1099 or corp-to-corp); contract-to-hire if there's a mutual fit. On-site requirement: You must be physically present in San Francisco during build-out and initial operations; local field work (e.g., UCSF) as needed. Compensation: Contract, $100-300/hour Timeline: Immediate start #J-18808-Ljbffr

A leading technology company in San Francisco is seeking a Machine Learning Engineer to ensure the quality and coverage of data across diverse languages. You will design large-scale datasets, evaluate models, and implement quality control systems. The ideal candidate has expertise in multilingual datasets and a strong background in applied ML. This full-time role offers competitive benefits, including fully covered insurance and in-office perks, in a supportive team environment. #J-18808-Ljbffr

Employment Type Full time Department Engineering About us At Eon, we are at the forefront of large-scale neuroscientific data collection. Our mission is to enable the safe and scalable development of brain emulation technology to empower humanity over the next decade, beginning with the creation of a fully emulated digital twin of a mouse. Role We're a San Francisco team collecting very large microscopy datasets and we need an expert to design and implement our end-to-end data pipeline, from high-rate ingest to multi-petabyte storage and downstream processing. You'll own the strategy (on-prem vs. S3 or hybrid), the bill of materials, and the deployment, and you'll be on the floor wiring, racking, tuning, and validating performance. Our current instruments generate data at ~1+ GB/s sustained (higher during bursts) and the program will accumulate multiple petabyes total over time. You'll help us choose and implement the right architecture considering reliability and cost controls. Outcomes (what success looks like) Within 2 weeks: Implement an immediate data-handling strategy that reliably ingests our initial data streams. Within 2 weeks: Deliver a documented medium-term data architecture covering storage, networking, ingest, and durability. Within 1 month: Operationalize the medium-term pipeline in production (ingest → buffer → long-term store → compute access). Ongoing: Maintain ≥95% uptime for the end-to-end data-handling pipeline after setup. Responsibilities Architect ingest & storage: Choose and implement an on-prem hardware and data pipeline design or a cloud/S3 alternative with explicit cost and performance tradeoffs at multi-petabyte scale. Set up a sustained-write ingest path ≥1 GB/s with adequate burst headroom (camera/frame-to-disk), including networking considerations, cooling, and throttling safeguards. Optimize footprint & cost: Incorporate on-the-fly compression/downsampling options and quantify CPU budget vs. write-speed tradeoffs; document when/where to compress to control $/PB. Integrate with acquisition workflows ensuring image data and metadata are compatible with downstream stitching/flat-field correction pipelines. Enable downstream compute: Expose the data to segmentation/analysis stacks (local GPU nodes or cloud). Skills 5+ years designing and deploying high-throughput storage or HPC pipelines (≥1 GB/s sustained ingest) in production. Deep hands-on with: NVMe RAID/striping, ZFS/MDRAID/erasure coding, PCIe topology, NUMA pinning, Linux performance tuning, and NIC offload features. Proven delivery of multi-GB/s ingest systems and petabyte-scale storage in production (life-sciences, vision, HPC, or media). Experience building tiered storage systems (NVMe ← HDD/object) and validating real-world throughput under sustained load. Practical S3/object-storage know-how (AWS S3 and/or on-prem S3-compatible systems) with lifecycle, versioning, and cost controls. Data integrity & reliability: snapshots, scrubs, replication, erasure coding, and backup/DR for PB-scale systems. Networking: ****25/40/100 GbE (SFP+/SFP28), RDMA/ RoCE/iWARP familiarity; switch config and path tuning. Ability to spec and rack hardware: selecting chassis/backplanes, RAID/HBA cards, NICs, and cooling strategies to prevent NVMe throttling under sustained writes. Ideal skills: Experience with microscopy or scientific imaging ingest at frame-to-disk speeds, including Micro-Manager-based pipelines and raw-to-containerized format conversions. Experience with life science imaging data a plus. Engagement details Contract (1099 or corp-to-corp); contract-to-hire if there's a mutual fit. On-site requirement: You must be physically present in San Francisco during build-out and initial operations; local field work (e.g., UCSF) as needed. Compensation: Contract, $100-300/hour Timeline: Immediate start #J-18808-Ljbffr

A data consulting firm based in San Francisco is seeking a Palantir Foundry Consultant for a contract position. The ideal candidate should have strong experience in Palantir Foundry, SQL, and PySpark, with proven skills in data pipeline development and ETL automation. Responsibilities include building data pipelines, implementing interactive dashboards, and leveraging data analysis for actionable insights. This on-site role offers an excellent opportunity for those experienced in the field. #J-18808-Ljbffr

An innovative tech firm in Berkeley seeks a Senior Data Engineer to manage complex data engineering pipelines. You will ensure data quality, support ML engineers across locations, and establish infrastructure standards. The ideal candidate has over 5 years of experience in Data Science or MLOps, strong algorithmic skills, and proficiency in GCP, Python, and SQL. This role offers competitive salary and the chance to impact a growing team in a dynamic field. #J-18808-Ljbffr

The Opportunity: As a data scientist, you're excited at the prospect of unlocking the secrets held by a data set, and you're fascinated by the possibilities presented by IoT, machine learning, and artificial intelligence. In an increasingly connected world, massive amounts of structured and unstructured data open new opportunities. As a data scientist at Booz Allen, you can help turn these complex data sets into useful information to solve global challenges. Across private and public sectors-from fraud detection to cancer research to national intelligence-we need you to help find the answers in the data. On our team, you'll use your data science expertise to help the client conduct training assessments and after actions based on data generated during live, virtual and constructive training. You'll work closely with clients to understand their questions and needs, and then dig into their data-rich environments to find the pieces of their information puzzle. You'll use the right combination of tools and frameworks to turn sets of disparate data points into objective answers to increase technical and tactical expertise of Marine Corps units. Ultimately, you'll provide a deep understanding of the data, what it all means, and how it can be used to improve Marine Corps training readiness. Work with us as we use data science for good. Join us. The world can't wait. You Have: 5+ years of experience with data exploration, data cleaning, data analysis, data visualization, or data mining 5+ years of experience with statistical and general-purpose programming languages for data analysis 5+ years of experience analyzing structured and unstructured data sources Experience developing predictive data models, quantitative analyses and visualization of targeted data sources Experience leading the development of solutions to complex programs Experience with natural language processing, text mining, or machine learning techniques Secret clearance Bachelor's degree Nice If You Have: Experience working with Marine Corps Live, Virtual and Constructive Training Systems Experience with distributed data and computing tools, including MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL Experience with visualization packages, including Plotly, Seaborn, or ggplot2 Clearance: Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; Secret clearance is required. Compensation At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page. Salary at Booz Allen is determined by various factors, including but not limited to location, the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $77,600.00 to $176,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen's total compensation package for employees. This posting will close within 90 days from the Posting Date. Identity Statement As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Work Model Our people-first culture prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely. If this position is listed as remote or hybrid, you'll periodically work from a Booz Allen or client site facility. If this position is listed as onsite, you'll work with colleagues and clients in person, as needed for the specific role. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.

Are you passionate about changing the world through machine learning and location intelligence? Join Esri as we leverage the IoT revolution and explosive growth of location data to help organizations extract advanced intelligence, predict significant events, and automate work processes using AI and machine learning. We're seeking a Data Scientist II who combines technical expertise with strong interpersonal skills to collaborate directly with customers, understand their unique challenges, and develop data-driven solutions that integrate seamlessly with GIS workflows. While prior GIS experience is not required, you'll work closely with GIS experts and gain valuable knowledge in spatial data analytics. We serve customers across diverse domains including natural resources, defense, commercial industries, public transportation, utilities, and governmental entities in over 160+ countries. Responsibilities Consult closely with customers to understand their needs and serve as Esri's sole representative Scope projects, communicate uncertainties and risks, and accurately estimate delivery times Develop and pitch data science solutions that map business problems to advanced analytics approaches Serve as a subject matter expert for internal stakeholders Write production-level code for data analysis and process automation Build high-quality analytics systems employing techniques from data mining, statistics, and machine learning Perform feature engineering, model selection, and hyperparameter optimization to achieve high predictive accuracy Deploy models to production environments and assist customers in troubleshooting implementations Implement best practices for geospatial machine learning and develop reusable technical components for demonstrations and rapid prototyping Design and implement generative AI solutions and agentic systems to enhance customer workflows and automate complex geospatial tasks Stay updated with the latest trends in machine learning, deep learning, and AI, and incorporate them into project delivery Quickly become proficient in unfamiliar fields to address diverse customer needs Requirements 2+ years of practical machine learning experience or relevant academic/lab work Hands-on relevant experience in generative AI, large language models, and agentic systems Exemplify tenacity, determination, creativity, curiosity, and independence in problem-solving Ability to learn new concepts quickly and implement new technologies rapidly Flexibility to adapt to diverse customer solutions in a rapidly evolving technical landscape Experience working with non-technical stakeholders in multiple domains Proficiency in building and optimizing supervised and unsupervised machine learning models, including deep learning techniques Fundamental understanding of mathematical and machine learning concepts Applied statistics experience Expertise in developing machine learning solutions, preferably in Python Proficiency in data extraction, transformation, and loading between various systems Ability to produce compelling data visualizations to convey results Strong communication skills, including the ability to explain technical concepts to non-technical audiences Capability to manage multiple projects simultaneously Bachelor's degree in mathematics, statistics, computer science, physics, or a related field Recommended Qualifications Master's degree or PhD in mathematics, statistics, computer science, physics, or a related field Experience with spatial and GIS concepts, preferably using Esri software Familiarity with Git, ML libraries such as PyTorch and TensorFlow, and transformers-based large language models (LLMs) Experience handling large-scale batch/streaming data with big data tools like Apache Spark Experience with cloud services (AWS, Azure, and more) #LI-MN1

corporate_fare Google place Mountain View, CA, USA Apply Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 8 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), or 5 years of experience with an advanced degree. Preferred qualifications: Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. About the job Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next. The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world. The US base salary range for this full-time position is $156,000-$229,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google . Responsibilities Perform analysis utilizing relevant tools (e.g., SQL, R, Python). Help solve problems, narrowing down multiple options into the best approach, and take ownership of open-ended ambiguous business problems to reach an optimal solution. Build new processes, procedures, methods, tests, and components with foresight to anticipate and address future issues. Report on Key Performance Indicators (KPIs) to support business reviews with the cross-functional/organizational leadership team. Translate analysis results to business insights or product improvement opportunities. Build and prototype analysis and business cases iteratively to provide insights at scale. Develop knowledge of Google data structures and metrics, advocating for changes where needed for product development. Influence across teams to align resources and direction. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire . Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes. #J-18808-Ljbffr

Salary: $200-250k base + RSUs This fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We're looking for a Staff Data Scientist to drive Sales and Go-to-Market (GTM) analytics, applying advanced modeling and experimentation to accelerate revenue growth and optimize the full sales funnel. About the Role As the senior data scientist supporting Sales and GTM, you will combine statistical modeling, experimentation, and advanced analytics to inform strategy and guide decision-making across our revenue organization. Your work will help leadership understand pipeline health, predict outcomes, and identify the levers that unlock sustainable growth. Key Responsibilities Model the Business: Build forecasting and propensity models for pipeline generation, conversion rates, and revenue projections. Optimize the Sales Funnel: Analyze lead scoring, opportunity progression, and deal velocity to recommend improvements in acquisition, qualification, and close rates. Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of pricing, incentives, and campaign initiatives. Advanced Analytics for GTM: Apply machine learning and statistical techniques to segment accounts, predict churn/expansion, and identify high-value prospects. Cross-Functional Partnership: Work closely with Sales, Marketing, RevOps, and Product to influence GTM strategy and ensure data-driven decisions. Data Infrastructure Collaboration: Partner with Analytics Engineering to define data requirements, ensure data quality, and enable self-serve reporting. Strategic Insights: Present findings to executive leadership, translating complex analyses into actionable recommendations. About You Experience: 6+ years in data science or advanced analytics roles, with significant time spent in B2B SaaS or developer tools environments. Technical Depth: Expert in SQL and proficient in Python or R for statistical modeling, forecasting, and machine learning. Domain Knowledge: Strong understanding of sales analytics, revenue operations, and product-led growth (PLG) motions. Analytical Rigor: Skilled in experimentation design, causal inference, and building predictive models that influence GTM strategy. Communication: Exceptional ability to tell a clear story with data and influence senior stakeholders across technical and business teams. Business Impact: Proven record of driving measurable improvements in pipeline efficiency, conversion rates, or revenue outcomes.

A technology finance firm in San Francisco is seeking an experienced Data Engineer. The role involves building data pipelines, integrating data across various platforms, and developing scalable web applications. The ideal candidate will have a strong background in data analysis, software development, and experience with AWS. The salary range for this position is between $160,000 and $210,000, with potential bonuses and equity. #J-18808-Ljbffr

Zyphra is an artificial intelligence company based in Palo Alto, California. The Role: As a Machine Learning Data Engineer - Systems & Retrieval, you will build and optimize the data infrastructure that fuels our machine learning systems. This includes designing high-performance pipelines for collecting, transforming, indexing, and serving massive, heterogeneous datasets from raw web-scale data to enterprise document corpora. You'll play a central role in architecting retrieval systems for LLMs and enabling scalable training and inference with clean, accessible, and secure data. You'll have an impact across both research and product teams by shaping the foundation upon which intelligent systems are trained, retrieved, and reasoned over. You'll work across: Design and implementation of distributed data ingestion and transformation pipelines Building retrieval and indexing systems that support RAG and other LLM-based methods Mining and organizing large unstructured datasets, both in research and production environments Collaborating with ML engineers, systems engineers, and DevOps to scale pipelines and observability Ensuring compliance and access control in data handling, with security and auditability in mind Requirements: Strong software engineering background with fluency in Python Experience designing, building, and maintaining data pipelines in production environments Deep understanding of data structures, storage formats, and distributed data systems Familiarity with indexing and retrieval techniques for large-scale document corpora Understanding of database systems (SQL and NoSQL), their internals, and performance characteristics Strong attention to security, access controls, and compliance best practices (e.g., GDPR, SOC2) Excellent debugging, observability, and logging practices to support reliability at scale Strong communication skills and experience collaborating across ML, infra, and product teams Bonus Skill Set: Experience building or maintaining LLM-integrated retrieval systems (e.g, RAG pipelines) Academic or industry background in data mining, search, recommendation systems, or IR literature Experience with large-scale ETL systems and tools like Apache Beam, Spark, or similar Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding-based retrieval Understanding of data validation and quality assurance in machine learning workflows Experience working on cross-functional infra and MLOps teams Knowledge of how data infrastructure supports training pipelines, inference serving, and feedback loops Comfort working across raw, unstructured data, structured databases, and model-ready formats Why Work at Zyphra: Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued We strongly value new and crazy ideas and are very willing to bet big on new ideas We move as quickly as we can; we aim to minimize the bar to impact as low as possible We all enjoy what we do and love discussing AI Benefits and Perks: Comprehensive medical, dental, vision, and FSA plans Competitive compensation and 401(k) Relocation and immigration support on a case-by-case basis On-site meals prepared by a dedicated culinary team; Thursday Happy Hours In-person team in Palo Alto, CA, with a collaborative, high-energy environment If you're excited by the challenge of high-scale, high-performance data engineering in the context of cutting-edge AI, you'll thrive in this role. Apply Today! #J-18808-Ljbffr