Top Senior Data Science Jobs in San Francisco Bay Area, CA

Lead the fraud data science function: define ML strategy, build and productionize fraud and lending-fraud models, run advanced analyses (clustering, network analysis), design A/B tests, integrate ML with rules engines, monitor models in cloud, and communicate results to stakeholders.

Lead the fraud data science function: define ML strategy, build and deploy real-time fraud detection models, investigate fraud patterns (including lending fraud), run experiments, collaborate with stakeholders, and maintain monitoring and dashboards to minimize fraud losses.

Lead SMB Ads analytics end-to-end: define growth KPIs, diagnose funnel friction, build propensity/LTV models, design attribution and A/B experiments, automate dashboards, and forecast advertiser/revenue growth to inform product and marketing decisions.

Lead measurement and insights for ads demand: define metrics and benchmarks, analyze advertiser performance, build early-warning signals, design experiments, translate findings into sales and product actions, forecast opportunities, and close feedback loops to improve advertiser outcomes.

Own the design and build of a data-quality and assurance function for a large-scale pricing intelligence data lake. Responsibilities include architecting observability, defining quality standards, building anomaly detection and health scoring, instrumenting pipelines, answering customer data-quality questions, and tracing issues to root causes across collection, processing, and source changes.

As the first Data Scientist, you will analyze user data and AI outputs to enhance content creation, define metrics, conduct A/B testing, and collaborate closely with cross-functional teams to improve product features.

Lead data science across autonomy, safety, and product by analyzing machine telemetry and operational data, defining metrics, designing experiments, and partnering with ML platform and engineering to deliver insights that improve system performance and safety.

Senior Data Scientist partnering with Product, Design, Engineering, Finance, and Marketing to drive user growth through data science, causal inference, experimentation, and analytics. Responsibilities include developing LTV and retention models, designing A/B tests, creating measurement frameworks and dashboards, translating complex analyses into recommendations, and strengthening data foundations for scalable experimentation.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Designs, validates, and operationalizes predictive, machine learning, and AI models using large structured and unstructured datasets. Performs statistical analysis, develops visualizations and dashboards, presents findings to stakeholders, and integrates solutions across teams. Provides clinical-program analytics, evaluates data science tools, mentors junior scientists, leads concurrent projects, and prepares end-to-end model pipelines for business use.

Lead data science across product and business areas, developing measurement frameworks, metrics, causal inference methods, forecasting models, and statistical analyses. Partner with product and business leaders on roadmap and growth decisions, apply AI and agentic workflows to accelerate analytics, communicate insights to senior stakeholders, and mentor other data scientists.

Leads data science and analytics initiatives supporting clinical development and precision medicine. Develops statistical methods, machine learning algorithms, prototypes, KPIs, reports, and predictive analyses. Partners with cross-functional teams to solve business problems, establish analytical standards, ensure regulatory and GCP compliance, and evaluate clinical trial progress and risks. Coaches junior team members and communicates technical insights to stakeholders.

Develop and maintain sustainability methodologies that translate climate science, GHG accounting standards, and regulatory requirements into auditable product logic. Collaborate with engineers, data scientists, product managers, customers, auditors, and stakeholders; review code, write technical specifications, contribute to implementations and testing, investigate complex sustainability data questions, and document scientific decisions. The role applies sustainability expertise to real-world business data and product capabilities.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

As a Data Scientist at Anthropic, you'll support data-driven decisions across the customer lifecycle by defining metrics, analyzing data, and developing statistical models. You'll collaborate with various teams to influence strategies and improve analytics infrastructure.

The Data Scientist role involves developing measurement models, writing production-quality code in Python, and collaborating with teams to optimize data pipelines and ML workflows.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.

Build and maintain data science models and quantitative measurement frameworks to guide infrastructure decisions (chargers, parking, depots). Analyze infra efficiency and identify improvements. Collaborate cross-functionally with Supply Operations, Product, and Engineering to incorporate business context into model-driven decisions supporting expansion to new territories and cities.

Lead the experimentation science roadmap for Waymo's marketplace platform. Design advanced causal inference and experimental methods for spatial-temporal and network effects, set best practices, partner with engineering to productionize pipelines, and translate results into clear product strategy for stakeholders.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Collects, explores, and analyzes large datasets to identify business opportunities and improve outcomes. Develops predictive models, machine-learning algorithms, testing frameworks, and data pipelines; evaluates data quality and model performance; and communicates actionable insights to stakeholders and clients. The role also manages data science projects, establishes reporting best practices, and collaborates with cross-functional teams to implement and monitor solutions.