Product Data Scientist (Growth)

Job description

## **Product Data Scientist (Growth)** **🎨 About OpenArt** OpenArt is an AI Storytelling and Visual Creation Platform used by millions worldwide. We’re building the next generation of creative tools powered by cutting-edge AI, enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we’re shaping that future. ### **🚀 Why Join OpenArt** - **Ride an inflection point.** OpenArt has grown **7–10X in revenue over the past 2 years** in one of the hottest categories in tech. Every experiment you run compounds against a steep trajectory. - **Own the experimentation practice** — you'll be the person who defines how OpenArt tests, learns, and ships product changes with real statistical rigor. - **Direct impact on the funnel.** Your work will shape activation, conversion, monetization, and retention for millions of creators. - **Meaningful equity, real upside** at an early-stage company with strong momentum. - **Cross-functional partnership** with Product, Design, Engineering, and Data team. - **Build from 0 → 1.** Experimentation infrastructure, growth measurement, and retention frameworks are being laid down now — you'll shape them. - High ownership, low process, fast iteration environment. ## **🎯 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. ## **🧑‍💻 What We’re Looking For** ### 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 ##