Product Data Scientist (Growth)

Description

The company runs an AI storytelling and visual creation platform used by millions of people worldwide.

It is building next-generation creative tools that let anyone make videos, visuals, characters and stories quickly with AI, and the team sees AI as the native basis of creativity.

The role is the analytical partner for PMs and engineers who ship changes to the product, turning data into decisions on activation, conversion, monetization and retention through experiments and behavioral insight.

You will work with Product, Engineering and Growth on experiments across signup, onboarding, paywall and pricing, feature adoption and retention loops, and run deep-dives on funnels, cohorts, LTV and retention drivers that shape what gets built.

Responsibilities

• Primary focus is experimentation: design, run and analyze A/B tests and other causal experiments on signup, onboarding, activation, monetization and retention.
• Set the company's standard for experimental rigor, including sample sizing, minimum detectable effect, run length, primary and secondary metrics and readout criteria.
• Diagnose broken tests such as sample ratio mismatch, novelty and interaction effects, multiple comparisons and segment differences.
• Build the experimentation practice as the company grows, including playbooks, guardrails and post-launch validation.
• Analyze the growth funnel from signup through activation, paid conversion, retention and resurrection.
• Find what separates users who stay from those who churn, and turn those findings into product recommendations.
• Run LTV and cohort analyses that inform pricing, packaging and monetization.
• Segment users by behavior to find the groups that matter and what drives them.
• Estimate the impact of proposed product changes before engineering starts building.
• Co-own key product metrics (activation, engagement, retention, monetization) with PMs.
• Build dashboards that leaders trust and keep metric definitions consistent across product, marketing and finance.
• Turn analyses into clear, firm recommendations for PMs, engineers and executives.
• Challenge experiments that are underpowered, badly scoped or measuring the wrong thing, backing your view with data.

Requirements

• 3-6 years of experience in data science or analytics supporting product, growth or lifecycle teams; level and title can vary by candidate.
• Deep A/B testing experience, with many experiments designed, run and analyzed.
• Strong SQL and Python, and the ability to handle data end to end without close guidance.
• Solid statistical foundation.
• Product sense and a habit of finding insights.
• Communication clear enough to explain results to a PM, an engineer or a CPO, and to hold your ground in debate.
• High ownership and comfort working with ambiguity.
• Experience as an early hire at a consumer startup.
• Experience with consumer products, marketplaces or subscription businesses.
• Experience with experimentation platforms, in-house or vendor such as Statsig or Optimizely.
• Familiarity with causal inference beyond A/B tests, such as difference-in-differences, synthetic control, propensity matching and CUPED.
• Experience with BigQuery, Amplitude, Metabase, Stripe and GCP.
• Exposure to AI/ML products or generative AI.
• Ability to work across time zones with a globally distributed team.

Conditions

Competitive base salary and bonus program.

Equity, giving you a real stake in what you build.

High autonomy in a fast-growing environment.

Bay Area preferred, with hybrid work allowed.

Visa sponsorship available.

Remote considered only for exceptional candidates.

Benefits

• Revenue has grown 7-10x over the past two years in one of the hottest categories in tech, so your experiments build on a steep upward trend.
• You define how the company tests, learns and ships product changes, with real statistical rigor.
• Your work directly shapes activation, conversion, monetization and retention for millions of creators.
• Meaningful equity at an early-stage company with strong momentum.
• Work alongside Product, Design, Engineering and Data.
• Build from zero: experimentation infrastructure, growth measurement and retention frameworks are being set up now, and you will shape them.
• High ownership, minimal process and quick iteration.