[All remote jobs](https://jobicy.com/jobs.md)Open role[![RevenueCat logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/09/e9b2999e3e7950f153097909d027dda2.jpg)](https://jobicy.com/company/revenuecat.md)Remote opportunity at[RevenueCat](https://jobicy.com/company/revenuecat.md) # Senior Data Scientist, Fraud & Risk Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/revenuecat.md)Share28 Aug 2026Published27Listing views3Application actions27 Sep 2026Apply before Opportunity details ## About this role. AI SummaryRevenueCat is seeking a Senior Data Scientist to serve as the founding data-science partner for its RC Capital fintech product line. The role will build and own underwriting, fraud detection, anomaly detection, and lead-qualification models using real-time app revenue data. This person will work closely with Product and Engineering to deploy risk signals into customer-facing flows and internal decisioning systems. Success includes delivering a baseline fraud model within three months, owning fraud and underwriting systems end to end, and influencing the Capital roadmap. The position requires strong Python, SQL, production ML, and financial-risk expertise in a highly autonomous remote environment. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a founding, high-stakes data-science role in financial services where models directly influence capital deployment and fraud exposure. It requires building production systems from an ambiguous starting point while balancing risk, growth, technical rigor, and speed. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$220,000US market range$180k–$260k0$286k AI insightThe disclosed annual salary is USD 220,000, with a median of USD 220,000 because the stated minimum and maximum are identical. For a US-market senior/principal-level data scientist specializing in fintech risk, fraud, and underwriting, an estimated base-salary market range is USD 180,000-260,000 annually; equity and other benefits may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [Fraud detection](https://jobicy.com/jobs?search_keywords=Fraud%20detection.md)[Credit underwriting](https://jobicy.com/jobs?search_keywords=Credit%20underwriting.md)[Financial risk modeling](https://jobicy.com/jobs?search_keywords=Financial%20risk%20modeling.md)[Anomaly detection](https://jobicy.com/jobs?search_keywords=Anomaly%20detection.md)[Lead qualification](https://jobicy.com/jobs?search_keywords=Lead%20qualification.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Machine learning deployment](https://jobicy.com/jobs?search_keywords=Machine%20learning%20deployment.md)[Model monitoring](https://jobicy.com/jobs?search_keywords=Model%20monitoring.md)[Fintech analytics](https://jobicy.com/jobs?search_keywords=Fintech%20analytics.md) Sample interview questionsHow would you approach building an initial fraud-detection model for Daily Payouts when labeled fraud data is limited?I would begin with a measurable baseline that combines available historical outcomes, expert-defined rules, and anomaly features such as refund behavior, revenue volatility, account age, payout patterns, and cross-platform inconsistencies. I would use a staged decisioning framework with manual-review thresholds, monitor precision and recall by segment, and create a feedback loop to improve labels and retrain the model. How do you balance approval growth with loss prevention in an underwriting model? I frame the problem around expected value rather than model accuracy alone, incorporating approval rate, expected loss, contribution margin, and customer impact. I would establish risk tiers and thresholds aligned to the company's risk appetite, evaluate performance through back-testing and controlled rollout, and adjust policies as portfolio outcomes mature. Describe how you would productionize and monitor a credit-risk model. I would version data, features, model artifacts, and decision policies; define clear service-level expectations; and deploy through a reproducible pipeline with rollback capability. Monitoring would cover data quality, feature and prediction drift, approval and loss outcomes, calibration, segment fairness, latency, and operational alerts, with scheduled and event-driven retraining criteria. What signals from mobile-app subscription data could be valuable for underwriting? Useful signals may include recurring revenue trends, subscription retention, trial-to-paid conversion, install growth, refund and chargeback rates, platform mix, revenue concentration, cohort performance, seasonality, and payout consistency. I would validate each signal for stability, leakage, causal plausibility, and incremental value before using it in production decisions. How would you communicate uncertainty around a new risk model to Product and leadership? I would clearly separate observed evidence, assumptions, and unknowns, then express trade-offs in business terms such as expected losses, approval volume, customer friction, and confidence intervals. I would recommend a reversible rollout plan with explicit success metrics, guardrails, review points, and decisions that can be revisited as more performance data becomes available. 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’re looking for a Data Scientist to support product initiatives for RevenueCat Capital, with deep experience in lead qualification, anomaly detection, fraud prevention, and underwriting. This is a chance to be the founding Data Scientist embedded within the Capital team, a greenfield opportunity to scale our new fintech business lines from <$1M to $100M+. You’ll partner directly with leadership, collaborate across teams, and bring a new product to life inside RevenueCat. Your work will touch real money, real developers, and a product that is just getting started. Read more about the opportunity [here](https://www.notion.so/RC-Capital-the-opportunity-33ccb1a108b080ea9fdef24dfa0caa06?pvs=21). ### The Opportunity: RC Capital RevenueCat is on a mission to help developers make more money. We build software that helps apps implement and manage purchases, with over 50% of new subscription apps on the App Store launching with RevenueCat. OpenAI’s mobile subscriptions run on RC. Top of funnel metrics are up 300%+ YoY. RC Capital is our next chapter: we’ll continue to help developers make more money, not just through software, but through financial products. Financial institutions would love to have what we already have: * Trust + distribution: App developers already trust us in their purchase flow — the highest stakes moment in any user journey. * Real-time, verifiable data: Cross-platform revenue plus leading indicators like installs, trials, conversions, and refunds. * Unmatched industry insights: We know how apps earn money at scale, which improves underwriting and product design. Our first product is [Daily Payouts](https://www.revenuecat.com/daily-payouts): a factoring product that lets developers get app store proceeds sooner. Scaling it is a big challenge, but it’s just the beginning. There’s so much more to build: credit cards, revenue-based lending, cohort-based financing. We’ve got a mountain to climb. ### What you’ll do * Own the Data Science strategy for RC Capital. Build the foundation for our underwriting and fraud detection systems from the ground up. Nothing is locked in, you’ll define the models, the signals, and the approach. * Develop sophisticated models for lead qualification, anomaly detection, fraud prevention, and credit underwriting. All this using the richest, most real-time app revenue data in the world. * Partner closely with Product and Engineering to integrate risk signals and underwriting logic into customer-facing flows and internal decisioning engines. * Analyze cross-platform revenue data to uncover insights that improve our underwriting models and product design. * Build mechanisms for measuring impact, evaluating model performance, and driving prioritization of new data initiatives. * Operate with high ownership in an ambiguous, fast-moving environment helping to define the long-term vision for our financial products. ### About you You are a Senior Data Scientist who cares deeply about impact and has direct experience with the kind of models that carry real financial consequence. From a skills perspective, you bring: * You have 5+ years of data science experience, ideally with a strong background in fintech, credit, lending, or payments. * You have deep expertise in fraud and/or underwriting. You know how to build models that balance risk and growth, and you understand the nuances of financial data. * You’re highly analytical and technical. You are an expert in SQL and Python. You can build, deploy, and monitor models in production. You don’t wait for someone else to pull the data. * You understand mobile apps or developer ecosystems, or you’re eager to learn this space and how apps earn money at scale. * You act like an owner. You aren’t afraid to roll up your sleeves and get something done yourself. You treat RevenueCat’s balance sheet, product, and brand like it’s your own. * You thrive in ambiguity. You are comfortable making low-information, high-stakes decisions. You can quickly get to confidence, move on, and iterate. * You’re a systems thinker. You can step back from the particular and see the process. You look for opportunities to automate and build things that scale, when you’ve had enough signal to know that you should. * You know when it’s good enough. You are obsessed with getting things right, but you know when you’re at diminishing returns. You balance detail, speed, and ambition without losing sight of impact. ### What success look like ### In the first month, you’ll: * Understand how Daily Payouts works today and what data powers it. * Get to know the team and the current state of our underwriting and fraud approach. * Form your own point of view on where the biggest gaps are. ### Within the first 3 months, you’ll: * Have a baseline fraud detection model in production. Imperfect is fine, measurable is required. * Learn the basics of incident response, and be part of the on-call rotation. * Work with Product and Engineering to integrate risk signals into real customer flows. * Define what “better” looks like so we have something to improve against. ### Within the first 6 months, you’ll: * Own the underwriting and fraud detection systems end to end. * Influence the RC Capital product roadmap with data-backed proposals on what to build next. * Be the person who knows best how RevenueCat’s revenue data translates into financial risk signals. ### Within the first 12 months, you’ll: * Lead new Data initiatives as the Capital product line expands beyond Daily Payouts. * Help shape how Data Science operates within Capital as the team grows. * Have had a material impact on how RevenueCat deploys capital and manages risk at scale. ### 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 Curious about the interview process? Discover more in [our blog post](https://www.revenuecat.com/blog/company/how-we-hire-at-revenuecat/) about how we hire and learn tips to help you succeed. 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