Product Data Scientist – Growth

OpenArt AI

. Design, run, and analyze A/B tests and other causal experiments across signup, onboarding, activation, monetization, and retention .

Posted 10/13/2026full-timeSan Carlos • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and analyzing A/B tests and causal experiments, with a strong foundation in SQL and Python. Capable of translating complex analyses into actionable product recommendations while maintaining rigorous experimental standards.

Highest-signal resume keywords

A/B TestingSQLPythonStatistical AnalysisProduct Metrics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

A/B TestingCausal InferenceStatistical AnalysisCohort AnalysisBehavioral SegmentationLTV AnalysisExperiment DesignData AnalysisMetric DefinitionDebugging

Soft Skills

CommunicationOwnershipInsight GenerationComfort in Ambiguity

Tools & Technologies

BigQueryAmplitudeMetabaseStripeGCPStatsigOptimizely

Industry Keywords

Consumer ProductsMarketplacesSubscription BusinessesGrowth AnalyticsLifecycle Teams

Tech Stack

Tools & technologies

BigQueryGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Design, run, and analyze A/B tests and other causal experiments across signup, onboarding, activation, monetization, and retention
Establish experimental rigor, including test sizing, MDE, run duration, metrics, and readout standards
Debug sample ratio mismatch, novelty effects, interaction effects, multiple comparisons, and segment heterogeneity
Build experimentation playbooks, guardrails, and post-launch validation processes
Analyze the growth funnel from signup through activation, paid conversion, retention, and resurrection
Identify retention and churn drivers and translate findings into product recommendations
Conduct LTV, cohort, behavioral segmentation, and opportunity-sizing analyses
Define and own product metrics for activation, engagement, retention, and monetization
Build and validate trusted dashboards
Maintain consistent metric definitions across product, marketing, and finance
Translate analyses into recommendations for PMs, engineers, and executives
Challenge underpowered, mis-scoped, or improperly measured experiments

Requirements

What you’ll need

3–6 years of experience in data science or analytics supporting product, growth, or lifecycle teams
Experience designing, running, and analyzing A/B tests and other causal experiments
Strong SQL and Python skills
Solid statistical foundation
Product intuition and insight-generation ability
Excellent communication skills
High ownership and comfort in ambiguity
Experience as an early hire at a consumer startup (nice to have)
Experience with consumer products, marketplaces, or subscription businesses (nice to have)
Experience with experimentation platforms such as Statsig or Optimizely (nice to have)
Familiarity with causal inference methods such as diff-in-diff, synthetic control, propensity matching, and CUPED (nice to have)
Experience with BigQuery, Amplitude, Metabase, Stripe, and GCP (nice to have)
Exposure to AI/ML products or generative AI (nice to have)
Comfort working across time zones with a globally distributed team (nice to have)

Benefits

Comp & perks

Competitive base salary and bonus program
Equity — meaningful ownership in what you build
High autonomy, high growth environment
Visa sponsorship available
Hybrid work setup in the Bay Area
Remote consideration for exceptional candidates