Top Hybrid Senior Level Data & Analytics Jobs in San Francisco, CA

The Computational Systems Biologist will design and execute complex computational research, analyze diverse clinical and molecular datasets, and develop machine learning models to enhance clinical insights. This role involves cross-functional collaboration and clear communication of technical results to non-technical audiences.

As a Principal Data Engineer, you will lead the development of a scalable data platform at Chime, overseeing architecture for diverse data needs, collaborating across teams, and ensuring compliance and governance. You will guide engineers in building efficient data solutions, and maintain best practices for data management and integration.

As a Senior Data Scientist at Roblox, you will enhance the performance of the game engine by establishing success metrics, building dashboards for monitoring, conducting A/B experiments, and collaborating with cross-functional teams to optimize user engagement and product features.

As a Staff Analytics Engineer, you will lead the design and implementation of scalable data solutions, aligning technical strategies with business objectives. Responsibilities include managing data transformations, mentoring team members, driving cross-functional collaboration, and enhancing data practices across the organization.

In this role, you'll leverage machine learning to build and deploy models to identify and mitigate risks. You'll collaborate cross-functionally with stakeholders while developing scalable frameworks to enhance analytical capabilities, and present insights to the company.

As a Decision Analytics Consultant at ZS, you will leverage quantitative skills to address client business analytics needs, collaborate with clients to identify information needs, analyze relevant data, and communicate findings succinctly. You will also develop expertise in specific client analytics areas while being actively engaged with clients during working hours.

As a Senior Data Engineer, you will develop and optimize large-scale data pipelines, write quality code for data processing, and enable access to data for machine learning initiatives. Responsibilities include building data infrastructures, automating workflows, and supporting the deployment of ML models while ensuring data governance and reliability.