Credit Risk Analytics Specialist

CRNCY Group

2d•$125 - $175

About The Position

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. 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.

Requirements

Experience helping a lender move from basic, rule-based underwriting to a more data-driven and risk-based credit model.
Experience in environments with basic, conditional, or one-size-fits-all credit rules, but strong repayment discipline, low risk tolerance, and high recovery performance.
Experience using SQL and Python to analyze application, loan, repayment, default, and collections data.
Experience with credit risk modeling, including probability of default, first-payment default, scorecards, and customer risk segmentation.
Experience with first-loan sizing and affordability analysis, including payment-to-income rules and loan amount calibration.
Experience with modeling techniques such as logistic regression, XGBoost, LightGBM, or similar practical machine learning methods.
Experience with cohort analysis and portfolio performance tracking, including delinquency, default, expected loss, repeat borrowing, and collections outcomes.
Experience with model validation and backtesting, including out-of-time testing and data leakage prevention.
Experience with scenario testing and controlled experiments, including champion/challenger testing, A/B testing, Bayesian testing, causal inference, or Monte Carlo simulation.
Experience with predictive customer value analysis, including repeat borrowing behavior, customer lifetime value, and risk-adjusted profitability.
Experience with analytics and decisioning tools, such as BigQuery, Power BI, dbt, Taktile, Provenir, Alloy, Zoot, or similar platforms.
Ability to use data and practical modeling methods to improve underwriting, first-loan sizing, and risk-reward decisions.
Practical, hands-on approach, able to work with existing data to solve immediate problems.
Ability to translate analysis into practical underwriting recommendations.

Nice To Haves

Experience operating in markets where external credit bureau data, open banking, cashflow tools, or alternative data providers are non-existent or not fully integrated.

Responsibilities

Improve credit rule calibration and first-time loan sizing.
Use historical application, loan, repayment, and collections data to assess current underwriting rules.
Identify instances of under-lending to strong customers, over-lending to higher-risk customers, or adverse selection.
Contribute to a more risk-based credit system, including customer segmentation, loan amount calibration, performance measurement, and risk-based pricing.
Improve underwriting in practical, step-by-step stages.
Use internal lending data to identify repayment patterns, customer risk, and affordability signals.
Calibrate loan amounts based on customer risk, income, payment capacity, and repayment behavior.
Introduce customer segmentation, scorecards, risk tiers, or probability-of-default models.
Test credit rule changes in controlled increments.
Use delinquency, default, collections, and repeat-borrowing data to improve underwriting decisions.
Support the move from flat pricing or one-size-fits-all offers toward risk-based pricing or variable rates.
Produce clear customer risk segments.
Develop better first-loan amount bands.
Identify under-lending pockets.
Provide recommendations for rule changes and approval thresholds.
Conduct scenario analysis showing expected impact on approvals, defaults, collections, conversion, and profit.
Create monitoring reports to track the effectiveness of changes.
Develop a practical roadmap toward risk-based pricing and scalable credit decisioning.