Data Scientist, Mail (US)

This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.

Company Overview

A leading AI productivity technology company focused on helping individuals and organizations work more efficiently. Its suite of AI-powered applications supports writing, email management, collaborative workspaces, databases, and proactive AI assistance. The company serves consumers, businesses, educational institutions, and enterprise organizations across a broad range of markets and languages.

Responsibilities

• Serve as the embedded Data Science partner for the core Mail product team, using data and experimentation to influence feature strategy, prioritization, and product roadmap decisions.
• Define and own key Mail metrics, including activation milestones, engagement depth, retention curves, and leading indicators of product and feature adoption.
• Design and run experiments throughout the product lifecycle, from onboarding and early activation through long-term engagement and habit formation.
• Develop measurement frameworks that distinguish meaningful product signals from statistical noise.
• Shape measurement strategies for AI-powered Mail features by developing quality frameworks and behavioral metrics that evaluate whether AI capabilities improve user productivity and effectiveness.
• Analyze user behavior to identify the features, experiences, and activation sequences that drive sustained engagement and power-user adoption.
• Partner with product-led and sales-led growth teams to identify factors that contribute to team expansion, conversion, and churn.
• Use behavioral insights to improve onboarding, feature discovery, engagement experiences, and product workflows.
• Analyze how capabilities such as scheduling, AI-powered workflows, and MCP-driven experiences influence how users interact with Mail.
• Communicate analytical findings to product managers, designers, engineers, ML engineers, and executives through experiment readouts, strategic analyses, and other data-driven presentations.
• Collaborate closely with Product, Engineering, Design, ML Engineering, and Experimentation teams to solve complex product and growth problems.
• Help establish and improve data-driven decision-making practices across the Mail organization.

Qualifications

• 5+ years of experience in Data Science with a demonstrated track record of driving measurable business and customer impact.
• Deep expertise in experimentation and causal inference.
• Strong knowledge of A/B testing, quasi-experimental methods, and observational techniques, including the ability to determine the appropriate methodology for different problems.
• Strong proficiency in Python and SQL, including data exploration, manipulation, and analysis.
• Strong applied statistics and machine learning skills with the ability to apply them to product and growth decisions.
• Ability to translate ambiguous business questions into effective experimental designs, measurement strategies, and meaningful metrics.
• Strong cross-functional communication and influencing skills, with the ability to turn technical insights into actionable recommendations.
• Self-directed and creative approach to problem-solving, with the ability to work effectively in ambiguous environments.
• Bachelor’s degree in statistics, mathematics, economics, computer science, data science, or another quantitative discipline.
• Advanced degree or equivalent practical experience is preferred.

Nice to Have

• Experience working at a fast-growing startup, with AI-native consumer products, and/or in B2B or SaaS environments.
• Experience serving as a strategic data partner to product, growth, or business leaders.
• Hands-on experience evaluating AI, LLM, or agentic products.
• Familiarity with AI-assisted development tools such as Claude Code or Codex, along with the ability to validate and supervise their output.
• Experience with experimentation platforms such as Statsig.
• Familiarity with modern data platforms such as Databricks.
• Experience with product-led growth, lifecycle analytics, and/or marketing analytics.
• Experience helping Data Science teams establish methodologies, standards, and collaborative processes.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1. No Endorsement: SpringCube does not endorse or recommend any company or position listed.
2. No Client Relationship: Listing a position does not mean SpringCube has a client, partnership, or employment relationship with the hiring company.
3. To Apply: Use the Apply button to apply directly through the relevant hiring company’s application process.
4. No Liability: SpringCube is not responsible for the hiring process, employment terms, recruitment decisions, or outcomes associated with any listed position.