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

As a Manager on the Revenue Analytics team, you will support a Revenue segment to drive growth strategy, optimize decision making, and execute analytics solutions. Responsibilities include establishing KPIs, performance benchmarks, and leading indicators, analyzing GTM motions, and scaling impact through data infrastructure.

In this role, you will develop an AI/ML strategy to enhance customer support at Canva, lead the creation of natural language systems, collaborate with engineering teams, and leverage machine learning capabilities across the organization.

The Sr. Competitive Intelligence Analyst at Exabeam will conduct competitive analysis in the SIEM sector, create and manage intelligence tools, collaborate with various departments to gather insights, and provide regular updates to senior leadership. The role demands staying updated on industry trends and enhancing CI processes.

As a Staff Media Revenue Growth Analyst at Babylist, you will support advertising revenue growth by performing data analyses, providing actionable insights, and shaping product offerings. Your role will involve strategic initiatives and stakeholder management, reporting to the Senior Manager of Marketing Analytics.

The VP of Data & Analytics will develop and lead a comprehensive data strategy, build high-performing data teams, foster a data-driven culture, and oversee data governance. This role involves collaboration with business leaders to identify opportunities, ensure data quality, and drive pricing strategies, leveraging advanced analytics and machine learning to deliver actionable insights for business growth.

As a Senior Machine Learning Engineer at Step, you will design, develop, and deploy ML models for Risk and Fraud detection. You will lead technical efforts, manipulate data with SQL, write robust code, design experiments for model performance evaluation, and collaborate with teams to enhance operations.

As a Senior Data Engineer at Iterable, you will own and optimize data pipelines for their machine learning platform, ensure effective handling of unstructured data, design ML systems, and collaborate with engineering teams to enhance data quality and infrastructure.