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Salary: USD 202,000 - 275,000 / annual
Superhuman (which now includes Grammarly) is seeking a Data Scientist to be the embedded analytics partner for Superhuman Mail, the fastest AI-native email platform serving 40+ million users. You'll work alongside product, engineering, design, and ML teams to shape Mail's growth, retention, and user delight through data-driven insights.
In this role, you'll own the metrics that matter for Mail—activation milestones, engagement depth, retention curves, and leading indicators for feature impact. You'll design and run experiments across the product lifecycle, from onboarding through long-term habit formation, and build measurement frameworks that separate signal from noise. A key focus is shaping the measurement strategy for Mail's AI features (Auto Drafts, Auto Labels, Auto Archive, Calendar, MCP, and agentic capabilities), defining quality frameworks and behavioral metrics that demonstrate whether AI is making users genuinely faster and more effective.
You'll identify what makes Mail sticky by analyzing which features drive deepest engagement, understanding the activation sequence for power users, and finding gaps in the funnel. You'll partner with product-led and sales-led growth teams to quantify levers that turn individual users into team expansions, and surface product signals that predict conversion and churn. Your insights will inform product moments—smarter onboarding flows, better feature discovery, and natural engagement nudges. You'll communicate findings clearly to PMs, designers, engineers, and executives, helping raise the bar on data-driven decision-making across the Mail team.
You'll work closely with the Experimentation team (using Statsig) in a fast-moving environment where the feedback loop is quick and the opportunity to shape how millions experience their inbox is real.
REQUIREMENTS:
- 5+ years of data science experience with a track record of driving measurable business and customer impact
- Deep expertise in experimentation and causal inference; fluency in A/B testing, quasi-experimental, and observational methods
- Strong proficiency in Python and SQL with excellent data exploration and manipulation skills
- 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
- Cross-functional influence and clear communication; ability to turn technical insight into action
- Self-starting, creative problem-solver who distills problems to their core and thrives with ambiguity
- Bachelor's degree in a quantitative field (statistics, mathematics, economics, computer science, data science, or similar); advanced degree or equivalent practical experience preferred
NICE TO HAVE:
- Experience at fast-growing startups, on AI-native consumer products, or in B2B/SaaS
- Strategic partnership experience with product, growth, or business leaders on data-driven decisions
- Hands-on evaluation of AI, LLM, or agentic product quality
- Comfort with AI-assisted development tools (Claude Code, Codex) and judgment to validate output
- Familiarity with Statsig or modern data stacks like Databricks
- Product-led growth, lifecycle, or marketing analytics experience
- Experience establishing methods, standards, and processes for data science team collaboration
