Collections Analyst

Roles and Responsibilities

• Own portfolio analytics end-to-end: vintage analysis, roll-rate/flow-rate tracking, liquidation curves, recovery/cure performance, and channel-wise contribution analysis to identify levers and risks.
• Design and optimize collections strategy across buckets and channels: segmentation, contact policy, allocation logic, and agency distribution; build an omni-channel allocation approach (who to contact, when, and via which channel).
• Run championchallenger experiments and controlled tests (scripts, channel mix, contact windows, offer structures, agency allocations) and track impact over time to evolve the best strategy.
• Define and track collections KPIs across operations and agencies (e.g., right-party contact, PTP and kept PTP, cure/roll-back, recovery, productivity, cost-to-collect) and translate insights into actions with call/field/digital teams.
• Develop and maintain predictive models in Python (e.g., propensity-to-pay, cure probability, contactability, channel selection) and productionize requirements with Tech/Engineering teams (data needs, score refresh cadence, monitoring).
• Build and own the collections measurement framework and reporting suite (Tableau): daily bucket dashboards, weekly performance packs, monthly vintage/roll-rate packs, agency scorecards, and settlement/legal trackers, with clear metric definitions and reconciliation logic.
• Work with multiple data sources (LMS, collections system, dialer, field app, payment rails, CRM) and drive requirements for data pipelines/ETL and data quality controls with Engineering.
• Support regulatory/audit-ready analytics for collections conduct and governance (complaints, calling compliance, agency adherence), enabling oversight and corrective actions.

What were looking for

• Strong collections analytics + strategy experience in BFSI/NBFC/fintech across delinquency buckets and channels (call, field, digital, settlements/legal).
• Proven capability in experimental design and performance tracking for collections strategies (championchallenger / test-and-learn).
• Strong hands-on SQL and Python; comfortable building Tableau dashboards and automating repeatable analytics workflows.
• Strong stakeholder leadership: ability to influence operations, vendors/agencies, risk, finance, and tech teams through data and clear decision frameworks.