Job Purpose

This role is responsible for driving the transformation of collections through AI/ML-driven strategies, leveraging a deep understanding of the end-to-end customer lifecyclefrom acquisition and cross-sell to collections. The role requires advanced machine learning expertise, strategic leadership, and deep domain knowledge across secured and unsecured financial products to design and implement data-driven strategies that optimize portfolio performance, customer outcomes, and collections effectiveness across both digital and field channels.

Key Responsibilities

Lead the design, development, and deployment of ML/DL models including, Risk of Flow Propensity (roll rate), Self-Pay Propensity & Time-to-Payment (TTP) Models and Ensure real-time integration with LOS/LMS and collections systems to deliver actionable insights at scale

Drive cost-effective resource allocation across both in-house and agency channels using predictive intelligence to maximize CE (Collections Efficiency) without incremental cost

Develop data-driven field strategies to identify high-priority customers, recommend Next Best Action, and enable route optimization for field teams to increase recoveries efficiently

Own the design & execution of digital-first strategies, leveraging propensity models to personalize channel allocation and interventions such as Advance EMI nudges, enhancing customer self-cure rates and reducing field dependency

Lead monthly performance variance analysis and deep-dive RCAs across acquisition and collection dimensions to explain deviations from plan and suggest course corrections.

Act as a bridge between analytics and business, presenting project outcomes, key insights, and data-driven recommendations to senior stakeholders and cross-functional leadership.

Qualifications

M. Tech / B.E / B.Tech / M. Sc in CS or Stats or Maths

Experience





Preferably 8+ years experience in performing descriptive and diagnostic analysis.

Data Science & Technical Expertise:

- Solid command of SQL and Python. Proficient in Tableau and Excel for building dashboards, automated MIS, and generating insights for business stakeholders
- Expertise in both supervised (regression, binary & multi-class classification) and unsupervised learning methods (e.g., clustering, dimensionality reduction)
- Familiarity with cutting-edge Gen-AI frameworks and ability to apply GenAI and Agentic AI frameworks to real-world collections use cases for operational efficiency. Knowledge of Azure AI is preferred & should have knowledge of framework like like Langchain, LLamaIndex, Autogen, Langgraph etc

Functional Competencies

- Experience working in finance industry & should understand end to end customer journey from acquisitions, cross-sell & collection.
- Should be able to build strategies across different stages of delinquency i.e. early, mid & late delinquency
- Build framework to increase digital collection for cost effectiveness.
- Guide the stakeholders by giving intelligence like whom to collect from, what should be the optimal strategy on field for profit maximisation, next best action to be taken on field, route optimisation & other advanced analytics framework that will help drive collection on field
- Month on month variance analysis for CXO review. Conduct a detail root cause analysis for not meeting the collection planned numbers (if any) not limiting only to collection dimensions. Other attributes in particular acquisition mix to be looked at while performing RCA
- Interact with Leadership team to showcase the projects under development/result of ad-hoc analysis as asked by the business or any interesting pattern you want to socialise.

Please share your updated resume at [email protected]

📌 Divisional Manager-Data Sciences-Chennai (Collection analytics)
🏢 TVS Credit Services
📍 Chennai