Applied Data Finance
Website:
applieddatafinance.com
Job details:
Role Summary
Senior leader accountable for credit underwriting, pricing, verification, account management, line management, and servicing strategy for our Returning Customer channel personal loan business. You will own the end-to-end credit risk lifecycle for existing customers—from re-underwriting and offer generation through line assignment, pricing, in-life account management, and delinquency/servicing strategy—leveraging ML/AI models, alternative data, and advanced analytics to drive profitable growth while keeping credit and fraud losses within risk appetite.
You will define and evolve the Returning channel credit strategy roadmap, govern policy and rule changes end-to-end, and serve as the senior credit strategy partner to Finance, Portfolio Management, Capital Markets, Product, Engineering, Legal, Compliance, Operations, Marketing, and Fraud. The role is strategy- and analytics-led with hands-on technical fluency to guide the team credibly and requires leading and growing a high-performing team of credit strategists and data scientists across India, while collaborating with colleagues and partners in the US.
How You'll Make an Impact
- Returning Channel Credit Strategy Roadmap: Own and evolve the credit, pricing, and verification strategy for the Returning channel—covering re-underwriting eligibility, pre-approval and pre-qualification, offer generation, cross-sell/up-sell, and refinance/consolidation strategies—balancing loss, approval, take-rate, unit economics, and customer experience to meet portfolio KPIs.
- Account Management & Line Management: Define and execute in-life account management strategies—credit line increases and decreases, APR repricing, authorization strategy, over-limit tolerance, retention offers, and reactivation of dormant accounts—grounded in behavioral scores, bureau triggers, and payment performance.
- Servicing & Collections Strategy Partnership: Partner with Servicing and Collections to optimize the trade-off between collections effort and returning-customer strategy—balancing loss mitigation, right-party treatment, and future eligibility/offer generation so that in-life and post-delinquency actions reinforce lifetime value; align treatments with credit segmentation and expected loss.
- Innovate with ML/AI and Alternative Data: Direct the development and deployment of ML/AI credit and behavioral models, champion/challenger scorecards, and alternative data (cashflow/bank transaction data, employment/income verification, trended bureau attributes) to continually improve underwriting precision and returns.
- Credit Risk Appetite & Loss Forecasting: Ensure credit losses remain within the company's defined risk appetite; oversee vintage-level loss forecasting and roll-rate analytics for the Returning book.
- Policy, Rule & Decisioning Governance: Govern credit policy, rules, cutoffs, and decisioning thresholds end-to-end—standards for proposal, review, approval, deployment, monitoring, retirement, and change control—with clear sign-off across Risk, Compliance, and Operations.
- Portfolio Monitoring & Segment Analytics: Analyze portfolio performance at a granular segment level (vintage, FICO/Vantage/Clarity band, product, term, loan amount, channel sub-segment, state) on an ongoing basis to identify key drivers, emerging trends, and required strategy actions.
- Executive Communication: Present data-driven recommendations to executive leadership and the Credit Risk Committee, translating complex credit analytics into clear, compelling narratives.
- Cross-Functional Collaboration: Partner with Finance, Portfolio Management, Capital Markets, Product, Marketing, Operations, Legal, Compliance, Engineering, and Fraud to design, implement, and monitor high-performing strategies; represent Returning channel credit in cross-functional prioritization.
- Vendor & Capability Discovery: Identify, evaluate, and build business cases for new bureaus, alternative data sources, verification vendors, and emerging technologies; own commercial trade-offs, benchmarking, and onboarding/retirement decisions.
- Test-and-Learn Strategy Development: Build and lead a strong test-and-learn discipline—champion/challenger, policy back tests, holdouts, A/B and multivariate tests, and uplift measurement - to quantify the impact of strategy changes on loss, approval, take-rate, and downstream lifetime value; institutionalize test design, sizing, readouts, and scale-up decisions.
- Regulatory & Compliance Alignment: Ensure Returning channel credit strategy is aligned with applicable US consumer lending regulations and guidance, and partner with Compliance, Legal, and Internal Audit; support adverse action reasoning, model risk management, audits, and regulatory exams.
- Team Leadership: Lead, coach, and grow a distributed team of credit strategists, analysts, and data scientists across US and India; set priorities, performance expectations, hiring plans, and career paths, and cultivate an innovative, open-minded team culture.
Qualifications
- Education: Bachelor's degree in a quantitative field (Statistics, Computer Science, Mathematics, Economics, Engineering, or related); advanced degree a plus.
- Experience: 12+ years managing credit risk within the Credit Card or Personal Loan sectors, with deep knowledge of credit and fraud risk management and strong business acumen; loss forecasting experience a strong plus.
- Returning-Book Depth: Demonstrated experience owning credit strategy for returning/existing customers—re-underwriting, pre-approval, cross-sell/up-sell, refinance, and account management.
- Servicing & Line Management: Working familiarity with account management, line management, and servicing/collections strategy—hardship programs, delinquency treatments, roll-rate management, and loss mitigation.
- Model & Scorecard Fluency: Strong working knowledge of ML/AI credit and behavioral models—how they are built, evaluated, and monitored (KS, PSI, gain/lift, drift, override rates)—with the ability to set strategy, interpret outputs, and direct technical partners.
- Hands-On Technical Skills: Proficiency in SQL and at least one of Python or R for independent data pulls, validation, and rapid analysis; comfort with cloud data warehouses (Snowflake/Databricks/Redshift), BI/visualization tools (Tableau, Looker, Power BI), and modern decisioning platforms (Provenir, Taktile, Zoot, or in-house engines).
- Leadership: 7+ years of people leadership experience, with a proven ability to mentor, develop, and inspire high-performing, innovative teams, including managing across geographies (US/India).
- Loss Forecasting: Experience with vintage-level loss forecasting and roll-rate analytics.
- Regulatory Knowledge: Working knowledge of US consumer lending regulation and model risk management (SR 11-7 principles).
- AI-Native Working Style: Comfort and demonstrated ability using AI tools (LLM assistants, code copilots, agentic workflows) as part of daily work to accelerate analysis, documentation, and decisioning.
- Exceptional Communication: Outstanding written and verbal communication; able to articulate complex analyses clearly and persuasively to internal stakeholders, external partners, investors, and executive leadership.
- Collaborative Mindset: Business-owner mentality and open-minded approach; strong relationship-builder who partners effectively across diverse functions and viewpoints.
- Agility: Demonstrated ability to thrive in a fast-paced environment, meet deadlines without compromising quality, and confidently address unfamiliar topics with clarity and conviction.
- Thought Leadership: Ability to build, refine, and elevate credit strategy frameworks through structured yet highly innovative approaches.
- Flexibility: Ability to work productively in a remote environment with colleagues in the US and India.
Preferred Qualifications
- Subprime/Near-Prime Depth: Subprime or near-prime consumer lending experience, or other high-loss-content credit products (installment, card, BNPL, auto).
- Function Build-Out: Experience standing up or materially upgrading a returning-channel or account-management credit strategy function, including operating rhythms, governance, and team build-out.
- Advanced Analytics Exposure: Familiarity with gradient-boosted models, uplift/causal modeling, reinforcement learning for pricing/line strategy, and real-time decisioning.
What Success Looks Like (First 12 Months)
- Documented Returning channel credit strategy roadmap tied to loss, approval, take-rate, and LTV KPIs.
- Refreshed account management and line management framework in production with measurable lift.
- Servicing-collections partnership optimized for the collections vs. returning-strategy trade-off, with measurable improvement in loss and returning-customer LTV.
- Upgraded ML/AI model and alternative data stack with clear performance attribution.
- Executive- and investor-ready monthly credit pack and Credit Risk Committee narrative in production.
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