Credit Risk Analytics Specialist

CRNCY Group is seeking a Credit Risk Analytics Specialist to help improve credit rule calibration and first-time loan sizing across our lending portfolio.

The main objective of this role is to use historical application, loan, repayment, and collections data to determine whether our current underwriting rules are properly sizing first loans and approving the right customers. The role will focus on identifying where we may be under-lending to strong customers, over-lending to higher-risk customers, or creating adverse selection through our current rules.

Over time, the role should help CRNCY move toward a more risk-based credit system, including stronger customer segmentation, better loan amount calibration, improved performance measurement, and eventually risk-based pricing or variable rates.

Requirements

Who we need

We need someone who has helped a lender move from basic, rule-based underwriting to a more data-driven and risk-based credit model. The ideal candidate has worked in environments where credit rules were basic, conditional, or one-size-fits-all, but where the business still maintained strong repayment discipline, low risk tolerance, and high recovery performance.

They should have experience with:

• Improving underwriting in practical, step-by-step stages rather than trying to rebuild the full system at once.
• Using messy internal lending data to identify repayment patterns, customer risk, and affordability signals.
• Calibrating loan amounts based on customer risk, income, payment capacity, and repayment behavior.
• Introducing customer segmentation, scorecards, risk tiers, or probability-of-default models.
• Testing credit rule changes in controlled increments before full rollout.
• Using delinquency, default, collections, and repeat-borrowing data to improve underwriting decisions.
• Supporting the move from flat pricing or one-size-fits-all offers toward risk-based pricing or variable rates.
• Operating in markets where external credit bureau data, open banking, cashflow tools, or alternative data providers are non-existent or not fully integrated.

Technical Skills Needed

The candidate should be able to use data and practical modeling methods to improve underwriting, first-loan sizing, and risk-reward decisions.

They should have experience with:

• SQL and Python to analyze application, loan, repayment, default, and collections data.
• Credit risk modeling, including probability of default, first-payment default, scorecards, and customer risk segmentation.
• First-loan sizing and affordability analysis, including payment-to-income rules and loan amount calibration.
• Modeling techniques such as logistic regression, XGBoost, LightGBM, or similar practical machine learning methods.
• Cohort analysis and portfolio performance tracking, including delinquency, default, expected loss, repeat borrowing, and collections outcomes.
• Model validation and backtesting, including out-of-time testing and data leakage prevention.
• Scenario testing and controlled experiments, including champion/challenger testing, A/B testing, Bayesian testing, causal inference, or Monte Carlo simulation.
• Predictive customer value analysis, including repeat borrowing behavior, customer lifetime value, and risk-adjusted profitability.
• Analytics and decisioning tools, such as BigQuery, Power BI, dbt, Taktile, Provenir, Alloy, Zoot, or similar platforms.

What Success Looks Like

A successful hire should be able to help CRNCY produce:

• Clear customer risk segments.
• Better first-loan amount bands.
• Identification of under-lending pockets.
• Recommendations for rule changes and approval thresholds.
• Scenario analysis showing expected impact on approvals, defaults, collections, conversion, and profit.
• Monitoring reports to track whether changes are working.
• A practical roadmap toward risk-based pricing and scalable credit decisioning.

What We Do Not Need

We are not looking for a general business/process analyst, a research-heavy data scientist, or someone who depends on perfect external data, credit bureaus, open banking, or advanced AI tools to produce useful insights. The right person must be practical, hands-on, and able to work with the data we have today to solve the immediate first-loan sizing and underwriting calibration problem before moving into more complex modelling or long-term optimization.

Benefits

This is a high-impact contract-to-hire role with the opportunity to help CRNCY build a scalable credit analytics and underwriting framework that can be applied across multiple regions. The successful candidate will work on practical lending problems that directly shape how we approve customers, size first loans, manage repayment risk, and expand access to credit.

The role offers meaningful exposure to real-world lending data, modern decisioning tools, and cross-functional teams across Credit, Operations, Data, Product, Collections, and senior leadership. The work will support CRNCY’s broader mission of using data to responsibly extend credit to customers who may be underserved, underbanked, or excluded from traditional banking channels.

This is a visible role where the candidate’s work will directly influence approvals, conversion, defaults, collections performance, customer experience, and risk-adjusted profitability.

Compensation

This is a contract-to-hire role with an expected hourly range of US$125–$175 per hour, depending on experience. Final compensation will be based on the candidate’s hands-on credit risk modeling experience, technical skillset, lending background, and ability to translate analysis into practical underwriting recommendations.