Senior data scientist jobs in Santa Rosa, CA - 527 jobs

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 cutting-edge AI search engine company in San Francisco is seeking a Data Partnerships specialist to build their data pipeline. The role involves owning the partnerships cycle, making strategic decisions, negotiating contracts, and potentially building a team. Candidates should have experience in contract negotiation and a Juris Doctor degree. This in-person role offers a competitive salary range of $160,000 - $250,000 with above-market equity. #J-18808-Ljbffr

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

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

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 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

A cutting-edge data analytics firm in San Francisco is seeking a full-stack engineer to enhance user experiences and integrate AI tools within their platform. You will work on innovative projects that shape data interactions, collaborate with teams on product initiatives, and tackle UX challenges. Ideal candidates should possess 3+ years of software engineering experience, proficiency in React and Typescript, and a strong desire to work in AI development. This position offers a competitive salary and benefits, with a hybrid work model. #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

Distinguished Data Engineers are individual contributors who strive to be diverse in thought so we visualize the problem space. At Capital One, we believe diversity of thought strengthens our ability to influence, collaborate and provide the most innovative solutions across organizational boundaries. Distinguished Engineers will significantly impact our trajectory and devise clear roadmaps to deliver next generation technology solutions.**About the Team:** Capital One is seeking a Distinguished Data Engineer, to work in our Credit Card Technology Data Engineering Team and build the future of financial services. We are a fast-paced, mission-driven group responsible for managing and leveraging petabytes of sensitive, real-time and batch data that powers everything from fraud detection models and personalized reward systems to regulatory compliance reporting. As a leader in Data Engineering, you won't just move data; you'll architect high-availability that directly influence millions of customer experiences and secure billions in transactions daily. You'll own critical data domains end-to-end, working cross-functionally with ML Scientists, Product Managers, and Business Analysts teams etc to solve complex, high-stakes problems with cutting-edge cloud technologies (like Snowflake, Kafka, and AWS). If you thrive on technical challenges, demand data integrity, and want your work to have a clear, measurable impact on the bank's core profitability and security, this is your team.This leader must have the ability to attract and recruit the industry's best talent, and simultaneously have the technical chops to ensure that we build compelling, customer-oriented solutions in an iterative methodology. Success in the role requires an innovative mind, a proven track record of delivering next generation software and data products, rigorous analytical skills, and a passion for delivering customer value through automation, machine learning and predictive analytics.**Our Distinguished Engineers Are:*** Deep technical experts and thought leaders that help accelerate adoption of the very best engineering practices, while maintaining knowledge on industry innovations, trends and practices* Visionaries, collaborating on Capital One's toughest issues, to deliver on business needs that directly impact the lives of our customers and associates* Role models and mentors, helping to coach and strengthen the technical expertise and know-how of our engineering and product community* Evangelists, both internally and externally, helping to elevate the Distinguished Engineering community and establish themselves as a go-to resource on given technologies and technology-enabled capabilities**Responsibilities:*** Build awareness, increase knowledge and drive adoption of modern technologies, sharing consumer and engineering benefits to gain buy-in* Strike the right balance between lending expertise and providing an inclusive environment where others' ideas can be heard and championed; leverage expertise to grow skills in the broader Capital One team* Promote a culture of engineering excellence, using opportunities to reuse and innersource solutions where possible* Effectively communicate with and influence key stakeholders across the enterprise, at all levels of the organization* Operate as a trusted advisor for a specific technology, platform or capability domain, helping to shape use cases and implementation in an unified manner* Lead the way in creating next-generation talent for Tech, mentoring internal talent and actively recruiting external talent to bolster Capital One's Tech talent**Basic Qualifications:*** Bachelor's Degree* At least 7 years of experience in data engineering* At least 3 years of experience in data architecture* At least 2 years of experience building applications in AWS**Preferred Qualifications:*** Masters' Degree* 9+ years of experience in data engineering* 3+ years of data modeling experience* 2+ years of experience with ontology standards for defining a domain* 2+ years of experience using Python, SQL or Scala* 1+ year of experience deploying machine learning models* 3+ years of experience implementing big data processing solutions on AWS***Capital One will consider sponsoring a new qualified applicant for employment authorization for this position***Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. #J-18808-Ljbffr

A leading global consulting firm seeks a Data Scientist - Director in San Francisco to drive AI solutions and data initiatives. The ideal candidate will lead multi-source data pipelines, architect complex data solutions while collaborating with business leaders. Candidates should have a strong educational background, extensive experience in data engineering, and proficiency with SQL and cloud-native infrastructure. This role offers a competitive salary range of $205,000 to $235,000 and promotes a hybrid working model. #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

A financial services company in San Francisco seeks a Distinguished Data Engineer to lead innovation in data architecture and management. The role involves building critical data solutions, mentoring teams, and leveraging cloud technologies like AWS. Ideal candidates will have significant experience in data engineering, a Bachelor's degree, and proficiency in modern data practices to drive customer value through analytics and automation. #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 expanding our data science organization to accelerate customer success after the initial sale-driving onboarding, retention, expansion, and long-term revenue growth. About the Role As the senior data scientist supporting post-sales teams, you will use advanced analytics, experimentation, and predictive modeling to guide strategy across Customer Success, Account Management, and Renewals. Your insights will help leadership forecast expansion, reduce churn, and identify the levers that unlock sustainable net revenue retention. Key Responsibilities Forecast & Model Growth: Build predictive models for renewal likelihood, expansion potential, churn risk, and customer health scoring. Optimize the Customer Journey: Analyze onboarding flows, product adoption patterns, and usage signals to improve activation, engagement, and time-to-value. Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of onboarding programs, success initiatives, and pricing changes on retention and expansion. Revenue Insights: Partner with Customer Success and Sales to identify high-value accounts, cross-sell opportunities, and early warning signs of churn. Cross-Functional Partnership: Collaborate with Product, RevOps, Finance, and Marketing to align post-sales strategies with company growth goals. Data Infrastructure Collaboration: Work with Analytics Engineering to define data requirements, maintain data quality, and enable self-serve dashboards for Success and Finance teams. Executive Storytelling: Present clear, actionable recommendations to senior leadership that translate complex analysis into strategic decisions. About You Experience: 6+ years in data science or advanced analytics, with a focus on post-sales, customer success, or retention analytics in a B2B SaaS environment. Technical Skills: Expert SQL and proficiency in Python or R for statistical modeling, forecasting, and machine learning. Domain Knowledge: Deep understanding of SaaS metrics such as net revenue retention (NRR), gross churn, expansion ARR, and customer health scoring. Analytical Rigor: Strong background in experimentation design, causal inference, and predictive modeling to inform customer-lifecycle strategy. Communication: Exceptional ability to translate data into compelling narratives for executives and cross-functional stakeholders. Business Impact: Demonstrated success improving onboarding efficiency, retention rates, or expansion revenue through data-driven initiatives.

At EY, we're all in to shape your future with confidence. We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Technology - Data and Decision Science - Data Engineering - Manager We are looking for a dynamic and experienced Manager of Data Engineering to lead our team in designing and implementing complex cloud analytics solutions with a strong focus on Databricks. The ideal candidate will possess deep technical expertise in data architecture, cloud technologies, and analytics, along with exceptional leadership and client management skills. The opportunity In this role, you will design and build analytics solutions that deliver significant business value. You will collaborate with other data and analytics professionals, management, and stakeholders to ensure that business requirements are translated into effective technical solutions. Key responsibilities include: Understanding and analyzing business requirements to translate them into technical requirements. Designing, building, and operating scalable data architecture and modeling solutions. Staying up to date with the latest trends and emerging technologies to maintain a competitive edge. Key Responsibilities As a Data Engineering Manager, you will play a crucial role in managing and delivering complex technical initiatives. Your time will be spent across various responsibilities, including: Leading workstream delivery and ensuring quality in all processes. Engaging with clients on a daily basis, actively participating in working sessions, and identifying opportunities for additional services. Implementing resource plans and budgets while managing engagement economics. This role offers the opportunity to work in a dynamic environment where you will face challenges that require innovative solutions. You will learn and grow as you guide others and interpret internal and external issues to recommend quality solutions. Travel may be required regularly based on client needs. Skills and attributes for success To thrive in this role, you should possess a blend of technical and interpersonal skills. The following attributes will make a significant impact: Lead the design and development of scalable data engineering solutions using Databricks on cloud platforms (e.g., AWS, Azure, GCP). Oversee the architecture of complex cloud analytics solutions, ensuring alignment with business objectives and best practices. Manage and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement. Collaborate with clients to understand their analytics needs and deliver tailored solutions that drive business value. Ensure the quality, integrity, and security of data throughout the data lifecycle, implementing best practices in data governance. Drive end-to-end data pipeline development, including data ingestion, transformation, and storage, leveraging Databricks and other cloud services. Communicate effectively with stakeholders, including technical and non-technical audiences, to convey complex data concepts and project progress. Manage client relationships and expectations, ensuring high levels of satisfaction and engagement. Stay abreast of the latest trends and technologies in data engineering, cloud computing, and analytics. Strong analytical and problem‑solving abilities. Excellent communication skills, with the ability to convey complex information clearly. Proven experience in managing and delivering projects effectively. Ability to build and manage relationships with clients and stakeholders. To qualify for the role, you must have Bachelor's degree in computer science, Engineering, or a related field required; Master's degree preferred. Typically, no less than 4‑6 years relevant experience in data engineering, with a focus on cloud data solutions and analytics. Proven expertise in Databricks and experience with Spark for big data processing. Strong background in data architecture and design, with experience in building complex cloud analytics solutions. Experience in leading and managing teams, with a focus on mentoring and developing talent. Strong programming skills in languages such as Python, Scala, or SQL. Excellent problem‑solving skills and the ability to work independently and as part of a team. Strong communication and interpersonal skills, with a focus on client management. Required Expertise for Managerial Role Strategic Leadership: Ability to align data engineering initiatives with organizational goals and drive strategic vision. Project Management: Experience in managing multiple projects and teams, ensuring timely delivery and adherence to project scope. Stakeholder Engagement: Proficiency in engaging with various stakeholders, including executives, to understand their needs and present solutions effectively. Change Management: Skills in guiding clients through change processes related to data transformation and technology adoption. Risk Management: Ability to identify potential risks in data projects and develop mitigation strategies. Technical Leadership: Experience in leading technical discussions and making architectural decisions that impact project outcomes. Documentation and Reporting: Proficiency in creating comprehensive documentation and reports to communicate project progress and outcomes to clients. Large-Scale Implementation Programs Enterprise Data Lake Implementation: Led the design and deployment of a cloud-based data lake solution for a Fortune 500 retail client, integrating data from multiple sources (e.g., ERPs, POS systems, e‑commerce platforms) to enable advanced analytics and reporting capabilities. Real‑Time Analytics Platform: Managed the development of a real‑time analytics platform using Databricks for a financial services organization, enabling real‑time fraud detection and risk assessment through streaming data ingestion and processing. Data Warehouse Modernization: Oversaw the modernization of a legacy data warehouse to a cloud‑native architecture for a healthcare provider, implementing ETL processes with Databricks and improving data accessibility for analytics and reporting. Ideally, you'll also have Experience with advanced data analytics tools and techniques. Familiarity with machine learning concepts and applications. Knowledge of industry trends and best practices in data engineering. Familiarity with cloud platforms (AWS, Azure, GCP) and their data services. Knowledge of data governance and compliance standards. Experience with machine learning frameworks and tools. What we look for We seek individuals who are not only technically proficient but also possess the qualities of top performers, including a strong sense of collaboration, adaptability, and a passion for continuous learning. If you are driven by results and have a desire to make a meaningful impact, we want to hear from you. What we offer you At EY, we'll develop you with future‑focused skills and equip you with world‑class experiences. We'll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more. We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $125,500 to $230,200. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $150,700 to $261,600. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options. Join us in our team‑led and leader‑enabled hybrid model. Our expectation is for most people in external, client serving roles to work together in person 40‑60% of the time over the course of an engagement, project or year. Under our flexible vacation policy, you'll decide how much vacation time you need based on your own personal circumstances. You'll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well‑being. Are you ready to shape your future with confidence? Apply today. EY accepts applications for this position on an on‑going basis. For those living in California, please click here for additional information. EY focuses on high‑ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi‑disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories. EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law. EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1‑800‑EY‑HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY's Talent Shared Services Team (TSS) or email the TSS at **************************. #J-18808-Ljbffr