Top Data Science Jobs in San Francisco Bay Area, CA

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

The Data Scientist will develop performance evaluation frameworks for autonomous vehicles, using statistical models and metrics to ensure quality and safety of the Waymo Driver, while collaborating with engineering teams on simulation data and product readiness.

The Data Scientist will build AI-driven fraud and risk systems, work with real-world data, and leverage graph techniques to improve detection and labeling. They will partner with engineering to scale data systems in the compliance domain.

Seeking a Data Scientist with expertise in Full stack Development and AWS Architecture for a 3-6 month local project.

The Bioinformatics Data Scientist will analyze multi-omics data, develop methods, build tools, and communicate findings to support drug development decisions.

Design and develop large-scale HD mapping datasets, data pipelines, and benchmarking tools. Collaborate with ML and cross-functional teams to curate data, improve model training strategies, and scale mapping and update workflows for autonomous vehicles.

Build full-stack data science solutions for finance: develop, test, and ship models; partner with data engineering and finance teams to create actionable financial insights; drive experimentation and analytics best practices; produce dashboards and reports to influence business decisions.

Lead and validate the firm's physical-risk signal through rigorous backtests, statistical modeling, and hypothesis-driven research. Translate hazard exposure into asset-level earnings impact, define methodology and assumptions, and guide product roadmap based on where the signal is strong or weak. Deliver end-to-end analyses, collaborate with founders, and communicate methods to stakeholders and buy-side partners.

Lead end-to-end clinical outcomes research using large EHR datasets: design observational and quasi-experimental studies, run causal inference analyses, interpret results, publish findings, and partner with clinicians, data science, and commercial teams to shape product and demonstrate impact.

Partner with Product and Engineering to define product metrics, design experiments and causal analyses, build reporting and models, analyze user behavior and segments, and translate insights into product recommendations while establishing Product Data Science practices.

The role involves building the marketing data science function, developing metrics, dashboards, and models to analyze marketing effectiveness, and fostering a data-informed culture.

Lead pricing analytics as the first dedicated data scientist on the Pricing team. Design analyses, causal models, experiments, and decision frameworks linking usage, conversion, retention, and revenue to guide pricing, packaging, discounts, and monetization across consumer, SMB, and enterprise segments. Communicate executive-ready recommendations and build a repeatable pricing data science function.

Lead product-focused data science work: define and track metrics, design and analyze A/B tests, evaluate model and agent performance across conversations and workflows, perform root-cause analyses, build statistical models and dashboards, and partner with Engineering and ML teams to improve instrumentation and data quality.

As a Data Scientist at Profound, you'll build predictive models, optimize AI-driven brand visibility, and turn data insights into actionable strategies. Requires proficiency in Python, R, and SQL, with 3+ years of analytics experience.

As a Data Scientist at Retool, you will analyze customer behavior and business performance, build analytical models, and provide insights to guide strategy across teams.

Partner with Slack GTM leaders to apply advanced analytics, experimentation, and predictive modeling to shape GTM strategy, measure impact, build scalable analytical frameworks, and translate insights for executives and cross-functional teams.

Embed with Product and Engineering to define success metrics, design measurement and experimentation frameworks, instrument and monitor launches, translate model-driven signals into decisions, and build repeatable decision loops to convert analysis into shipped changes.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.

Own revenue and product analytics: build KPIs, dashboards, dbt-style data models, run product/usage experiments, partner with GTM, and deliver executive insights to scale revenue operations.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead the fraud data science team to design, build, deploy, and monitor real-time fraud detection models. Perform feature engineering, unsupervised analysis, A/B testing, stakeholder collaboration, and dashboarding to reduce fraud losses and improve model performance and explainability.

Lead measurement and analysis for Notifications: define metrics, size opportunities, and communicate product recommendations. Partner with product, engineering, and ML to prioritize projects, improve notification ML models, and iterate via A/B experimentation. Mentor other data scientists and drive high-impact, cross-functional initiatives.

Join a SignalFire talent network to be matched with VC-backed startups seeking Senior/Staff Data Scientists. Responsibilities include experimental design and analysis, predictive and causal modeling, productionizing models, building metrics and dashboards, advising product and business teams, mentoring other data scientists, and shaping data strategy and tooling at fast-moving startups.

Drive pricing and yield optimization at GoFundMe by applying economics, behavioral science, and machine learning. Lead experimentation, model decision-making, and translate insights into action, while ensuring high standards for modeling and collaboration with product teams.

As a Data Scientist on Codex, you will enhance AI developer tools by defining developer productivity, running experiments, and analyzing user metrics to guide product direction.