Data Scientist, Product

About the role

What you’ll do

• Design and analyse A/B tests and quasi-experiments, with pre-registered metrics and clear statements of uncertainty.
• Build cohort and retention models in SQL and Python, and check each one against raw event counts before reporting it.
• Estimate the effect of agent suggestions on task completion, using matched controls where randomisation is not possible.
• Write analyses that a product manager can act on, with the chart, the caveat and the recommendation on one page.
• Partner with engineering on event schemas, so that new features log the fields needed to answer the next question.
• Maintain the metric definitions for the team, and retire dashboards that nobody uses or trusts.

What you’ll bring

• Experience in applied data science or analytics for consumer or B2B software, with a focus on product decisions.
• Strong SQL and Python, including pandas or similar, and comfort with statistics beyond basic significance testing.
• Working knowledge of experiment design, including power analysis, variance reduction and novelty effects.
• A habit of stating limits in plain words, and of saying when a result is too small to act on.
• Experience presenting findings to non-technical colleagues, and changing a decision because of them.

Nice to have

• Experience with causal inference methods such as difference-in-differences, instrumental variables or uplift modelling.
• Background in Bayesian analysis or hierarchical models for sparse product data.
• Familiarity with evaluation design for machine learning systems, including offline metrics and human review.
• Experience with a modern analytics stack, including dbt, a warehouse and an experimentation platform.

Work you might take on

1. 01

   Estimate the causal effect of a proactive reminder on missed meetings, using a staggered rollout and a clear pre-period.

2. 02

   Build a trusted-outcome metric that counts an agent action as successful only if no undo follows within 24 hours.

3. 03

   Analyse which onboarding steps predict retention at week six, and report the uncertainty around each step's effect.

4. 04

   Create a correction-cost dashboard that tracks minutes spent fixing agent errors, broken down by action type and team.

Compensation

$180K – $280K

The US base salary range for this role. Every offer also includes equity in Clexerl and, when it spins out, in the venture you help build. We tell you the reasoning behind your number.

Benefits

• Health, fully covered

  Medical, dental and vision for you and your dependants, premiums paid in full.

• Two kinds of equity

  Equity in Clexerl, plus a stake in the venture you help build when it spins out.

• 22 weeks parental leave

  Fully paid, for every parent, however your family arrives.

• Rest that sticks

  Flexible time off with a four-week minimum we actually check on, plus a company-wide week in December.

• Learning and compute

  A yearly budget for courses, conferences and books, and compute credits for side projects.

• 401(k) with a 4% match

  Matched from your first paycheque and vested immediately.

How we hire

1. Apply 5 minutes

   Send links to what you have built and a few lines on why this role. No cover letter needed.

2. Intro call 30 minutes

   A conversation with the hiring manager about the work, the team and what you want next.

3. Work sample 2–4 hours

   A paid take-home or a pairing session on a real-shaped problem. You pick the format.

4. Studio day One day

   Meet the team, go deep on your work and ours. Remote or in person.

5. Offer Within 5 days

   A written offer with the full range, the equity and the reasoning behind both.