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OpenArt is an AI-native storytelling and visual creation platform used by millions worldwide, building next-generation creative tools for video, visual, character, and story creation. The company has grown 7–10X in revenue over the past two years in a hot AI/creative category.
You'll be the analytical partner that Product, Engineering, and Growth teams rely on to ship product changes with statistical rigor. This role sits at the intersection of experimentation, product analytics, and growth strategy, with a primary focus on turning data into decisions that move the funnel: activation, conversion, monetization, and retention.
PRIMARY RESPONSIBILITIES:
Experimentation: Design, run, and analyze A/B tests and causal experiments across signup, onboarding, activation, monetization, and retention surfaces. Set the bar for experimental rigor—define test sizing, minimum detectable effect (MDE), run duration, metrics, and readout standards. Debug complex test issues including sample ratio mismatch, novelty effects, interaction effects, and segment heterogeneity. Build and evolve OpenArt's experimentation playbooks and guardrails as the company scales.
Growth & Retention Insights: Conduct deep-dive analyses on the growth funnel (signup → activation → paid conversion → retention → resurrection). Own retention analysis to identify drivers of long-term retention vs. churn and translate findings into product recommendations. Perform LTV and cohort analyses that shape pricing, packaging, and monetization decisions. Conduct behavioral segmentation to identify high-value user segments and their drivers. 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 leadership trusts. Ensure metric definitions remain consistent across product, marketing, and finance.
Communication & Influence: Translate analyses into crisp, opinionated recommendations that PMs, engineers, and executives act on. Push back with data when experiments are underpowered, mis-scoped, or measuring the wrong thing.
You'll work closely with Product, Design, Engineering, and the Data team in a high-ownership, low-process, fast-iteration environment. This is a 0→1 opportunity: experimentation infrastructure, growth measurement, and retention frameworks are being built now, and you'll shape them.
REQUIREMENTS:
- 3–6 years of experience in data science or analytics supporting product, growth, or lifecycle teams
- Solid A/B testing depth; you've designed, run, and analyzed many experiments
- Strong SQL and Python; comfortable working with data end-to-end independently
- Solid statistical foundation
- Product intuition and insight-generation instinct
- Excellent communication; ability to tell clear data stories to PMs, engineers, and executives
- High ownership and comfort with ambiguity
NICE TO HAVE:
- Early-hire experience at a consumer startup
- Experience with consumer products, marketplaces, or subscription businesses
- Familiarity with experimentation platforms (Statsig, Optimizely, etc.)
- Knowledge of causal inference methods beyond A/B tests (diff-in-diff, synthetic control, propensity matching, CUPED)
- Experience with BigQuery, Amplitude, Metabase, Stripe, or GCP
- Exposure to AI/ML products or generative AI
- Comfort working across time zones with a globally distributed team
Tech stack: BigQuery, SQL, Python, Amplitude, Metabase, Stripe, GCP.
