Decision Scientist

CRNCY Group

4d • Remote

About The Position

Help CRNCY become the world’s best underwriter of credit risk using incomplete, noisy, unstructured, and alternative data. CRNCY serves customers who are often underbanked, thin-file, or locked out of traditional lending options. Many have real repayment capacity but lack the formal credit history, documentation, or banking footprint that traditional lenders require. The challenge is to build a decision framework that helps identify good borrowers from imperfect information and lend more money to more people with the same or lower risk. This role is about making better lending decisions under uncertainty, focusing on questions like how much information is enough, which underwriting requirements create value, what risks are worth taking, how to lend more while losing less, how to identify creditworthy customers traditional lenders miss, and how to align loan amount, pricing, risk, expected loss, and profitability. The first mission is First-Time Loan Sizing, aiming to identify which first-time customers can responsibly support higher offers, recommend better first-loan amount bands, and design controlled tests to validate changes without weakening portfolio discipline.

Requirements

• Naturally think in probabilities, trade-offs, and expected value.
• Uncomfortable with rules that exist only because “that’s how we’ve always done it.”
• Instinctively ask: What is the probability of this outcome? What is the cost if it happens? What is the cost of preventing it? Is the risk worth the reward? What is the economically rational decision?
• Interested in decision quality, not just prediction.
• Worked in environments where decisions had to be made under uncertainty using incomplete or imperfect data.
• Degree in Decision Science, Risk Management, Economics, Statistics preferred.
• Experience with decision science, risk optimization, lending strategy, or portfolio economics.
• Experience with customer segmentation, expected value analysis, risk-adjusted returns, or pricing optimization.
• Experience with credit risk, underwriting analytics, scorecards, probability of default, first-payment default, expected loss, or repayment behavior analysis.
• Experience with insurance-related risk work such as actuarial pricing, underwriting analytics, risk selection, loss forecasting, claims analytics, fraud detection, or risk-based pricing.
• Experience with alternative data, behavioral data, unstructured data, or thin-file customer environments.
• Experience with experimentation, causal inference, A/B testing, champion/challenger testing, Bayesian testing, or Monte Carlo simulation.
• Experience using messy internal data to improve real business decisions.
• Technical enough to work with data, test assumptions, and answer practical modeling questions.
• SQL and Python.
• Probability, statistics, segmentation, and predictive modeling.
• Logistic regression, scorecards, XGBoost, LightGBM, or similar practical models.
• Cohort analysis, vintage analysis, expected loss, customer lifetime value, and portfolio performance tracking.
• Backtesting, out-of-time validation, data leakage prevention, and scenario testing.

Responsibilities

• Improve lending decisions under uncertainty.
• Evaluate which underwriting rules and requirements reduce risk versus create friction.
• Identify where CRNCY can safely lend more to strong first-time customers.
• Quantify trade-offs between approval growth, conversion, risk, expected loss, recoveries, and profitability.
• Translate data, models, and experiments into practical underwriting decisions.
• Design controlled tests to validate changes before full rollout.
• Help CRNCY move toward risk-based loan sizing, pricing, and scalable credit decisioning.

Benefits

• Remote working environment
• Exposure to emerging-market lending
• Close collaboration with senior leadership
• Opportunity to help build a durable underwriting advantage using alternative data, behavioral data, internal data, and real-world outcomes.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior