Data Scientist, Mail

Superhuman•San Francisco, CA

16d•$202,000 - $275,000•Remote

About The Position

The Data Scientist is embedded in Superhuman Mail’s core product team and partners with Data Engineering, Finance, RevOps, UX Market Research, product managers, engineers, designers, ML engineers, and the Experimentation team. The role helps shape product strategy, prioritization, and roadmap decisions through evidence and measurement, with a focus on acquisition, activation, retention, and expansion. The position is remote-flexible, with a hybrid setup available for team members based in San Francisco, New York City, or Seattle. Superhuman Mail is an AI-native email product designed to help people manage inboxes faster, stay focused on important messages, and collaborate. The Data Scientist will define success metrics, design and analyze experiments across the product lifecycle, and develop measurement approaches for Mail’s AI capabilities. The role also investigates user engagement and retention, identifies opportunities in individual-to-team growth, and uses behavioral insights to inform onboarding, feature discovery, and engagement experiences. Findings are communicated to audiences ranging from product teams to executives. This role is part of Superhuman’s broader integrated product platform, which includes Grammarly, Mail, Docs, Databases, and Go. The company’s products serve consumers and enterprise customers across varied user groups, organizations, languages, and markets.

Requirements

At least 7 years of data science experience and a track record of measurable impact for businesses and customers.
Deep expertise in experimentation and causal inference, including A/B testing and quasi-experimental and observational methods, with sound judgment about which to use.
Fluency in Python and SQL, with strong data exploration and manipulation skills.
Strong applied statistics and machine learning skills used to drive product and growth decisions.
Ability to translate ambiguous business questions into sound experimental designs, measurement plans, and metrics.
Ability to influence cross-functional partners and turn technical insights into action through clear communication, empathetic building, and openness to ideas.
Self-starting, creative problem-solving skills, ability to distill problems to their core, and comfort with ambiguity.
Bachelor’s degree in a quantitative field such as statistics, mathematics, economics, computer science, or data science, or a similar field.

Nice To Haves

Advanced degree or equivalent practical experience.
Experience at a fast-growing startup, with AI-native consumer products, and/or in B2B or SaaS.
Experience as a strategic thought partner to product, growth, or business leaders, driving data-informed decisions.
Hands-on experience evaluating AI, LLM, or agentic product quality.
Comfort with AI-assisted development tools such as Claude Code or Codex, including judgment to validate and supervise their output.
Familiarity with experimentation platforms such as Statsig and modern data stacks such as Databricks.
Experience with product-led growth and/or lifecycle and marketing analytics.
Experience establishing methods, standards, and collaboration processes for data science teams.

Responsibilities

Partner with the Mail core product team to shape feature strategy, prioritization, and roadmap decisions using data.
Define and own metrics for activation, engagement, retention, and feature impact.
Design and run experiments throughout the Mail product lifecycle, and build frameworks to distinguish meaningful effects from noise.
Develop measurement strategies, quality frameworks, and behavioral metrics for Mail’s AI features and agentic capabilities.
Analyze feature engagement, power-user activation paths, and funnel opportunities to understand what drives product retention and value.
Partner with product-led and sales-led growth teams to identify factors that drive individual-to-team expansion and signals that predict conversion and churn.
Use behavioral insights to inform onboarding, feature discovery, and engagement nudges, including experiences involving Calendar scheduling and MCP-driven workflows.
Communicate experiment results and strategic analyses clearly to product managers, designers, engineers, and executives.
Work with the Experimentation team using its Statsig platform to support fast experimentation and learning.