Data Scientist, Mail (US)

Superhuman

1d•$202,000 - $275,000•Hybrid

About The Position

This Data Scientist position is embedded in the Superhuman Mail business unit, working alongside Data Engineering, Finance, RevOps, and UX Market Research. Together, you act as one numbers-and-insights team, with a direct line to the leaders setting strategy and a shared mandate to move the metrics that matter. Superhuman Mail is the fastest, most AI-native email experience ever built. It helps knowledge workers and teams fly through their inbox twice as fast, stay on top of what matters most, and collaborate without friction — saving users four hours every single week. Where email has barely changed in decades, Superhuman Mail is reimagining it from the ground up: AI that drafts replies in your voice, auto-labels and triages incoming messages, surfaces follow-ups before you drop the ball, and can even send emails on your behalf. It serves everyone, from individual professionals to Fortune 500 sales teams, and boasts a deeply loyal user base and a product people genuinely love. The data environment is rich, the growth questions are hard, and the opportunity to drive real impact — on acquisition, activation, retention, and expansion — is enormous. Your work on Mail is part of a broader platform story. The Superhuman Platform suite includes Grammarly's writing assistance, Mail, Docs, Databases, and Go, our proactive AI assistant. As a compound startup, we're building an integrated suite rather than separate tools — so insights you surface in Mail ripple across the full product portfolio, and cross-product growth loops connect the dots between how users discover, adopt, and expand across the suite. Our products serve both consumers and enterprises, creating an unusually rich data environment. Our user base spans students and knowledge workers, individual users and Fortune 500 companies, universities and global enterprises, across a growing range of languages and markets. That breadth creates no shortage of important questions to own. This is an opportunity for those drawn to complex, high-stakes growth problems — in a product people are passionate about — who want their work to measurably change outcomes.

Requirements

5+ years of data science experience and a track record of driving measurable impact for business and customers.
Deep expertise in experimentation and causal inference. Fluent in A/B testing as well as quasi-experimental and observational methods, and know when to use each.
Fluent in Python and SQL, with strong data exploration and manipulation skills.
Strong applied statistics skills and machine learning skills, and use them to drive product and growth decisions.
Can translate ambiguous business questions into sound experimental designs, measurement plans, and metrics.
Influence cross-functional partners and turn technical insight into action through clear communication, empathetic building, and openness to ideas from anywhere.
A self-starting, creative problem-solver who distills problems to their core and thrives with ambiguity.
A bachelor’s degree in a quantitative field (statistics, mathematics, economics, computer science, data science, or a similar field); an advanced degree or equivalent practical experience is preferred.

Nice To Haves

Experience working at a fast-growing startup, on AI-native consumer products, and/or in B2B/SaaS.
Been a strategic thought partner to product, growth, or business leaders, driving data-driven decisions.
Evaluated the quality of AI, LLM, or agentic products hands-on.
Comfortable using AI-assisted development tools like Claude Code or Codex to move faster, and have the judgment to validate and supervise their output.
Familiar with experimentation platforms such as Statsig and modern data stacks such as Databricks.
Experience with product-led growth and/or lifecycle and marketing analytics.
Helped data science teams establish the methods, standards, and processes they use to collaborate.

Responsibilities

Be the embedded data science partner for the Mail core product team — shaping feature strategy, prioritization, and roadmap decisions with evidence, not just fielding ad hoc requests.
Define and own the metrics that matter for Mail: activation milestones, engagement depth, retention curves, and the leading indicators that tell us whether a new feature is actually changing how people work in their inbox.
Design and run experiments across the Mail product lifecycle — from onboarding and first-week activation to long-term habit formation — and build the measurement frameworks that separate signal from noise.
Shape the measurement strategy for Mail's AI features — Auto Drafts, Auto Labels, Auto Archive, Calendar, MCP, and the agentic capabilities on the roadmap — by defining quality frameworks and behavioral metrics that show whether AI is making users genuinely faster and more effective.
Figure out what makes Mail sticky: which features drive the deepest engagement, what the activation sequence looks like for users who become power users, and where in the funnel we're leaving value on the table.
Partner with product-led and sales-led growth to identify and quantify the levers that turn individual users into team expansions — and surface the product signals that predict conversion and churn.
Use data to craft remarkable product moments — turning behavioral insight into smarter onboarding flows, better feature discovery, and engagement nudges that feel natural rather than forced. This includes understanding how features such as Calendar scheduling and MCP-driven workflows change the way users interact with Mail on a day-to-day basis.
Communicate findings clearly to PMs, designers, engineers, and executives — from experiment readouts to strategic deep-dives — and help raise the bar on how the Mail team makes decisions with data.
Work alongside product managers, engineers, designers, and ML engineers, and in close partnership with our Experimentation team (running on Statsig), whose platform lets us ship, test, and learn quickly.

Benefits

Excellent health care (including a wide range of medical, dental, vision, mental health, and fertility benefits)
Disability and life insurance options
401(k) matching
Paid parental leave
20 days of paid time off per year
12 days of paid holidays per year
Two floating holidays per year
Flexible sick time
Generous stipends (including those for caregiving, pet care, wellness, your home office, and more)
Annual professional development budget and opportunities