Decision Scientist

Mission

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.

Our challenge is to build a decision framework that helps us identify good borrowers from imperfect information and lend more money to more people with the same or lower risk.

The Challenge

This role is about making better lending decisions under uncertainty.

We want to answer questions such as:

How much information is enough to make a good credit decision?

Which underwriting requirements create value, and which create unnecessary friction?

What risks are worth taking?

How do we lend more while losing less?

How do we identify creditworthy customers traditional lenders miss?

How do we align loan amount, pricing, risk, expected loss, and profitability?

First Mission: First-Time Loan Sizing

Our current underwriting rules have helped control risk and maintain strong recoveries. However, we believe we may be under-lending to strong first-time customers because our rules are still too conservative, conditional, and one-size-fits-all.

Your first mission will be 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.

What You’ll Do

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.

Requirements

The Type of Person We Need

You naturally think in probabilities, trade-offs, and expected value.

You are uncomfortable with rules that exist only because “that’s how we’ve always done it.” You 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?

You are not just interested in prediction. You are interested in decision quality.

Ideal Background

The ideal candidate has worked in environments where decisions had to be made under uncertainty using incomplete or imperfect data. A degree in Decision Science, Risk Management, Economics, Statistics would be preferred.

Strong candidates may have experience with:

Decision science, risk optimization, lending strategy, or portfolio economics.

Customer segmentation, expected value analysis, risk-adjusted returns, or pricing optimization.

Credit risk, underwriting analytics, scorecards, probability of default, first-payment default, expected loss, or repayment behavior analysis.

Insurance-related risk work such as actuarial pricing, underwriting analytics, risk selection, loss forecasting, claims analytics, fraud detection, or risk-based pricing.

Alternative data, behavioral data, unstructured data, or thin-file customer environments.

Experimentation, causal inference, A/B testing, champion/challenger testing, Bayesian testing, or Monte Carlo simulation.

Using messy internal data to improve real business decisions.

Technical Capabilities

This is not a pure data science research role. However, you must be technical enough to work with data, test assumptions, and answer practical modeling questions.

Helpful capabilities include:

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.

What This Role Is Not

This is not a general business analyst role, a pure machine learning research role, or a role for someone who needs perfect bureau data, open banking, or fully automated cashflow tools before producing useful insights.

We need someone who can work with imperfect information, think clearly about risk and reward, and help us make economically rational credit decisions.

Benefits

This role offers the opportunity to help change the lives of people who are underbanked, thin-file, or often overlooked by traditional lenders. By building better credit decisioning frameworks, you will help CRNCY identify customers with real repayment capacity and give them access to more appropriate financial products.

You will work on decisions that directly affect loan approvals, first-loan offers, customer experience, repayment performance, and responsible credit access. The work is high-impact, highly visible, and tied to a mission that goes beyond optimization: helping more people access credit fairly while managing risk intelligently.

CRNCY offers a remote working environment, exposure to emerging-market lending, close collaboration with senior leadership, and the opportunity to help build a durable underwriting advantage using alternative data, behavioral data, internal data, and real-world outcomes.