Product Data Scientist (Growth)

About the Role

We're looking for a Product Data Scientist focused on growth and retention to be the analytical partner PMs and engineers rely on when they ship changes to OpenArt's product experience.

This role sits at the intersection of experimentation, product analytics, and growth strategy. Your primary job is to turn data into product decisions that move the funnel - activation, conversion, monetization, and retention - through rigorous A/B testing and sharp behavioral insights.

You'll work closely with Product, Engineering, and Growth - designing and analyzing experiments across signup, onboarding, activation, paywall/pricing, feature adoption, and retention loops. You'll also do the deep-dive analyses that shape what teams decide to build in the first place: funnel diagnostics, cohort behavior, LTV segmentation, and retention driver analysis.

What You’ll Do

Experimentation (Primary)

• Design, run, and analyze A/B tests and other causal experiments across signup, onboarding, activation, monetization, and retention surfaces.
• Set the bar for experimental rigor at OpenArt - help decide test sizing, MDE, run duration, primary/secondary metrics, and readout standards.
• Debug and resolve tricky test issues - sample ratio mismatch, novelty effects, interaction effects, multiple comparisons, segment heterogeneity.
• Build and evolve OpenArt's experimentation practice as we scale - playbooks, guardrails, and post-launch validation.

Growth & Retention Insights

• Deep-dive analyses on the growth funnel: signup > activation > paid conversion > retention > resurrection.
• Own retention analysis - identify what drives long-term retention vs. churn, and translate that into product recommendations.
• LTV and cohort analyses that shape pricing, packaging, and monetization decisions.
• Behavioral segmentation - find the user segments that matter and what drives their behavior.
• Opportunity sizing - quantify the potential impact of proposed product changes before engineering builds them.

Metrics & Measurement

• Define and own key product metrics (activation, engagement, retention, monetization) alongside PMs.
• Build and validate dashboards that leaders trust.
• Ensure metric definitions stay consistent across product, marketing, and finance.

Communication & Influence

• Translate analyses into crisp, opinionated recommendations that PMs, engineers, and execs act on.
• Push back with data when a proposed experiment is underpowered, mis-scoped, or measuring the wrong thing.

Core Requirements

• 3-6 years of experience in data science or analytics supporting product, growth, or lifecycle teams (level and title flex based on candidate).
• Solid A/B testing depth. You've designed, run, and analyzed a lot of experiments.
• Strong SQL and Python - comfortable working with data end-to-end without hand-holding.
• Solid statistical foundation
• Product intuition and insight-generation instinct
• Excellent communication - you can tell a clear data story to a PM, an engineer, or a CPO, and hold your own in a room with strong opinions.
• High ownership and comfort in ambiguity.

Nice to Have

• 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: Statsig, Optimizely, etc.).
• Familiarity with causal inference methods beyond A/B tests (diff-in-diff, synthetic control, propensity matching, CUPED).
• Experience with our stack: BigQuery, Amplitude, Metabase, Stripe, GCP.
• Exposure to AI/ML products or generative AI.
• Comfort working across time zones with a globally distributed team.

Tech Stack You’ll Work with

BigQuery, SQL, Python, Amplitude, Metabase, Stripe, GCP

Compensation

• Competitive base salary and bonus program
• Equity - meaningful ownership in what you build
• High autonomy, high growth environment

Work Setup

• Bay Area preferred (hybrid allowed)
• Visa sponsorship available
• We’ll consider remote for exceptional candidates