About this role.
RevenueCat is hiring a Senior Product Data Scientist to own data-driven product opportunities across subscription analytics and monetization. The role combines proactive product discovery, production modeling, experimentation, forecasting, and stakeholder influence. The successful candidate will partner closely with Product, Engineering, and Analytics while shipping customer-facing capabilities such as LTV prediction and benchmarking. This is a high-ownership remote role for an experienced scientist who can turn ambiguous customer and business questions into measurable product outcomes.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would begin by exploring behavioral, funnel, and customer-segment data for meaningful deviations or unmet needs. After validating the signal and estimating potential impact, I would frame a hypothesis, align with product partners on a minimal solution, and define success metrics before shipping. I would then monitor adoption, model performance, and downstream product outcomes to iterate or revise the approach.
I would first define the target LTV horizon, prediction unit, eligibility criteria, and intended customer decisions. I would build leakage-safe features from subscription events, retention behavior, pricing, platform, and cohort attributes, then compare interpretable baselines with more advanced models using time-based validation. Evaluation would cover calibration, error by customer segment, stability over time, and whether the feature improves decisions or outcomes for developers.
I start by clarifying the decision, primary metric, guardrails, unit of randomization, and likely sources of bias. I assess sample-size feasibility and, where appropriate, use sequential or Bayesian methods that communicate uncertainty clearly and support practical decision-making. If randomized testing is not feasible, I would use carefully designed quasi-experimental methods and explicitly document assumptions and limitations.
I would lead with the decision and customer impact rather than the method. I would use a concise visual and plain-language explanation of the observed effect, uncertainty, segment differences, and recommended action. Technical details would be available in an appendix or follow-up discussion, ensuring stakeholders can act confidently without overstating certainty.
I treat the model as a software and product system: reproducible data transformations, versioned code and features, automated tests, documented assumptions, and a reliable deployment path. I define monitoring for data drift, prediction quality, latency, failures, and business outcomes, with clear ownership and rollback procedures. I also schedule regular evaluation to ensure the model remains useful as customer behavior and product workflows change.
RevenueCat removes the headaches of building and scaling in‑app subscriptions. Since graduating from YC’s S18 batch we’ve grown into the default monetization platform for mobile: we’re in >40% of newly shipped subscription apps, we process $12B+ in annual purchase volume, and we help everyone from a solo dev in Brazil to the OpenAI mobile team understand and grow their revenue.
We’re a remote‑first crew of 150+, spread across 25+ countries, and guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance. If you want your work to touch hundreds of millions of end‑users (and help the developers behind them get paid), you’ll fit right in.
The role
We are looking for a Senior Product Data Scientist who is deeply product-minded and highly proactive.
This is not a role for someone who waits for perfectly defined questions or works in isolation. We are looking for someone who actively looks at our data, understands our customers’ pain points, identifies opportunities, and pushes ideas forward.
You will work closely with Product, Engineering, and Analytics to shape what we build, why we build it, and how we measure success. Your work will directly power customer-facing features such as LTV prediction, experimentation and statistical significance, benchmarking, and entirely new data-driven products we have not built yet.
You will be expected to bring ideas to the table, backed by analysis and clear hypotheses, and to influence product direction through data.
Our data stack includes a Python backend, PostgreSQL production databases, Snowflake, dbt, and AWS.
What you will do
Proactively explore RevenueCat’s data to identify customer problems, opportunities, and product bets.
Translate ambiguous product and business problems and questions into clear analyses, models, and recommendations.
Partner with Product Managers to shape roadmaps, not just execute on them.
Design, build, and ship production-grade predictive and descriptive models that power customer-facing features.
Define and evaluate statistical approaches for experimentation, benchmarking, and forecasting.
Communicate insights clearly and persuasively to technical and non-technical audiences, with a focus on customer impact.
Continuously iterate on shipped models and features based on real-world usage and feedback.
This role has real ownership. You will not just support decisions, you will help drive them.
About you
You are a Senior Data Scientist who cares deeply about impact and product outcomes.
From a skills perspective, you bring:
5+ years of experience working as a Data Scientist.
Strong SQL skills and comfort with data modeling.
Experience building and deploying predictive and descriptive models in production.
Experience writing or collaborating on production-ready Python code.
A solid understanding of statistics and experimentation, ideally including Bayesian approaches.
The ability to clearly explain complex ideas and results to broad audiences.
You recognize yourself in several of these:
You are highly proactive and opinionated, and you are comfortable pushing ideas forward.
You enjoy working with messy, real-world data and imperfect information.
You care more about creating customer value than academic elegance.
You are comfortable operating in ambiguity and building structure where none exists.
You enjoy working closely with Product teams and influencing decisions.
You are excited by the consumer subscription ecosystem and curious about how developers make money.
What success looks like
In the first month, you’ll:
Understand our data models.
Get to know the team.
Ramp up on the ongoing data feature work.
Implement and ship your first project.
Within the first 3 months, you’ll:
Meaningfully contribute to shipping a data feature to thousands of developers.
Learn the basics of incident response, and be part of the on-call rotation.
Work with our Product, Analytics and Engineering teams to improve our data pipelines for data science features.
Launch your own explorations into our data to fulfill your own curiosity.
Within the first 6 months, you’ll:
Own one or more core data-powered features end to end.
Influence the data feature roadmap with clear, data-backed proposals.
Be a go-to partner for product teams on data-driven decision making.
Within the first 12 months, you’ll:
Propose and lead entirely new data-driven product initiatives.
Push the boundaries of how RevenueCat uses data to help developers grow revenue.
Help shape how data science operates at RevenueCat as the function grows.
What we offer:
Competitive equity in a fast-growing, Series C startup backed by top-tier investors, including Y Combinator
10-year window to exercise vested equity options
Fully remote and flexible work environment
4-5 weeks of suggested time off annually for mental, physical, and emotional recharge
$2,000 USD for workspace setup and $1,000 USD annual stipend for continuous learning
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