Collections Risk Analyst

DigniFi•Grapevine, TX

1d•Onsite

About The Position

As a DigniFi Collections Risk Analyst, you'll support collections performance and strategy through portfolio analytics and performance reporting. You'll analyze delinquency trends across stages and vintages, evaluate multi-channel contact strategies (including AI voice agents), and provide data-driven insights that help optimize recovery outcomes while maintaining our commitment to customer respect. Working with SQL and Python daily, you'll design performance analyses, support A/B testing of new technologies and approaches, and collaborate with Collections leadership, Credit Risk, and Compliance to identify what's working and where strategies can improve. This role offers significant exposure to AI-enabled collections technology while building deep expertise in consumer lending risk management as we help customers navigate unexpected vehicle repair costs.

Requirements

Bachelor’s degree in Finance, Economics, Statistics, Mathematics, Data Science, or related quantitative field.
Minimum 3–5 years of experience in collections analytics, credit risk analytics, or portfolio analytics (consumer lending preferred).
Demonstrated experience using SQL and Python (or R) in a professional analytics environment.
Experience with delinquency analysis, roll rates, loss forecasting, vintage analysis, or similar portfolio analytics.
Exposure to collections software platforms, CRM systems, or call-center/servicing data environments.
Strong understanding of consumer lending products, collections workflows, and operational performance metrics.
Experience designing or evaluating A/B tests or controlled experiments.

Responsibilities

Produce recurring collections performance reporting (delinquency migration, roll rates, cure rates, recovery rates, net credit loss) across stages and vintages.
Analyze cohort and vintage performance to identify emerging risks, behavior shifts, and early warning indicators.
Support creation of executive-level summaries for collections leadership.
Maintain, validate, and enhance reporting accuracy and data controls.
Analyze performance of AI voice agents using metrics such as RPC, completion rates, escalation rates, sentiment scoring, intent recognition, and FCR.
Support A/B testing of contact strategies, voice agent configurations, channel sequencing, timing, and messaging frameworks.
Evaluate multi-channel collections performance (voice, SMS, digital self-service) to support contact strategy optimization.
Provide analytical support for cost-benefit analysis, ROI, and performance trends for AI-enabled collections tools.
Identify potential compliance concerns by reviewing call/interaction performance trends and escalate when appropriate.
Segment delinquent accounts using payment behavior, prior outcomes, risk indicators, and exposure/Value-at-Risk.
Analyze propensity-to-pay patterns to distinguish self-cure accounts from high-risk segments.
Support strategy recommendations for treatment paths, contact intensity, channel selection, hardship eligibility, and escalations.
Contribute to analysis of delinquency trends, roll rates, and recovery curves for loss forecasting and CECL-related inputs.
Support forecasting variance analysis and capacity planning discussions.
Provide data and insights that connect collections performance to broader credit and financial metrics.
Analyze operational performance (RPC rates, contact rates, promise-to-pay kept rates, agent/AI productivity, campaign outcomes).
Identify bottlenecks or opportunities for process and strategy optimization.
Support ad-hoc analytics for policy changes, new treatments, and technology initiatives.
Collaborate with credit risk, data/BI, and servicing teams to ensure aligned metric definitions and shared understanding of portfolio health.
Partner with compliance and legal on analytics related to regulatory controls and contact strategy governance.
Engage with technology and vendor teams as needed to access data, support performance troubleshooting, and inform configuration changes.