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

As a Senior Data Analyst II, you will support growth initiatives by analyzing revenue metrics, automating workflows, and providing insights to drive customer success.

As a Staff Data Scientist, you will monitor and analyze personal loans underwriting models, driving data-driven decisions and enhancing model performance through deep analytical insights.

As a Senior Data Scientist at AKASA, you will analyze deep product data, focusing on improving GenAI products in healthcare revenue cycle management, evaluating performance, and guiding product strategy using statistical methods.

Lead and mentor a data science team delivering AI-driven insights and predictive analytics for enterprise-level business applications, ensuring strategies align with operational excellence.

The Senior Data Scientist will design and implement data-driven initiatives, optimize product offerings using AI/ML, and collaborate with engineers on scalable solutions.

The Analytics Lead will drive the evolution of data infrastructure and reporting, manage data governance, and mentor team members. Responsibilities include designing databases, developing ETL pipelines, and ensuring data quality across HR platforms while collaborating with stakeholders for data-driven decisions.

The Senior Data Scientist will analyze business problems, guide data visualization, and ensure statistical rigor while collaborating with cross-functional teams at Coinbase.

The Senior Data Scientist will lead the development of analytics products for revenue cycle operations, mentor junior team members, and ensure scalable data solutions. Responsibilities include building metrics frameworks, executing complex analyses using SQL and Python, and collaborating with engineering teams to enhance client insights and relationships.

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

Lead development of a greenfield data product by partnering with customers and product teams to build analytics and models (simulation, Bayesian networks, optimization, ML). Maintain existing analytics, improve predictive churn models, and translate customer needs into production-grade SQL/dbt pipelines, dashboards, and data-driven product features.