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About This Role
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
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At OpenArt AI, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
OpenArt AI AI Hiring
OpenArt AI has 1 open AI role right now. They're hiring across Data Scientist. Based in San Carlos, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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