Data Scientist, Mail

Superhuman, Hub - San Francisco +2

The role

For this role on the Mail team, Superhuman offers a remote-flexible work model, whereas other Data Science roles may be aligned with a specific hub based on business needs. This gives team members plenty of focus time and the flexibility to do their best work wherever they're based, while staying closely connected to a collaborative, tight-knit team culture. A hybrid setup is available for those based in San Francisco, New York City, or Seattle.

Want to use data science to shape how one of the most beloved productivity products in the world grows, retains, and delights its users? 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.

Posted pay

$202K–$275K

a year

Compared with similar jobs

This base pay is above the median for data science jobs in the Bay Area with no level in the job title.

Median: $170K a year
Other jobs in the group: 112

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location

Hub - San Francisco +2

Work type

Hybrid

Level

Not given

Experience

7+ years

Skills in the job text

Posted

20 days ago

Last check

3 h ago

Job type

Data science, Inference & ML systems, ML / AI engineering

The role

What you will do

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.

You'll do this 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. We think from first principles and stay with the hardest product problems through the messy middle. Mail is a product people are genuinely passionate about — the questions are hard, the feedback loop is fast, and the opportunity to shape how millions of people experience their inbox is real.

Data Scientist, Mail

The role

Data Scientist, Mail

The role

What you will do

What you need

Nice to have

Pay and benefits

About Superhuman

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