Top Data & Analytics Jobs in San Francisco Bay Area

Senior Manager, Data Scientist role at Capital One focusing on machine learning for consumer identity. Responsibilities include building machine learning models, partnering with business teams, and writing software to analyze customer data. Ideal candidate is innovative, creative, technical, and statistically-minded.

Lead teams to drive strategic direction through collaboration with Applied Science, Engineering and Product leaders. Partner with cross-functional team to deliver AI-powered products, engage in high impact applied research, and translate complex work into tangible business goals.

GoodRx is looking for a Sr. Data Engineer II to work on building data pipelines and processes, collaborating with teams to guide business decisions, and making smart engineering decisions based on data auditing. The role involves improving infrastructure scalability, efficiency, and reliability.

The Staff Data Engineer at Square Banking will work on building a Financial Data Mesh Platform using real-time Big Data technologies. Responsibilities include designing data processing systems, building core datasets, addressing data quality issues, and collaborating with cross-functional teams to accelerate decision-making processes.

Seeking a Senior Machine Learning Engineer to contribute to shaping the future of ML/AI at TaskRabbit. Responsible for building scalable ML infrastructure, pipelines, monitoring services, and collaborating with engineering teams. Requires 3+ years of experience and expertise in Python.

As a Senior Data Analyst at Vouch, you will play a key role in building the foundational data structure, tools, and models to drive data-driven decision-making in the insurance industry. Responsibilities include collaborating with cross-functional teams, developing analytical frameworks, and leading data projects end-to-end. The ideal candidate will have 3-6 years of experience in a quantitative analytical role and a degree in statistics.

Develop and implement machine learning models for fraud detection, analyze large datasets, collaborate with cross-functional teams, monitor and evaluate model performance, and stay up-to-date with the latest machine learning and fraud detection techniques.

Designs, develops, and programs methods and systems to analyze big data sources and generate insights and solutions. Interacts with teams to identify data analysis questions and issues. Cleanses and integrates large datasets from multiple sources. Identifies meaningful insights and communicates findings to managers.