Description

Key Responsibilities

Machine Learning & Analytics

• Design, develop, validate, and deploy propensity models for collections and customer behavior prediction.
• Build predictive models to identify customers with varying probabilities of payment, delinquency, default, or recovery.
• Apply advanced statistical and machine learning techniques to solve business problems.
• Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.
• Monitor model performance and recommend enhancements to improve prediction accuracy.
• Develop segmentation strategies and analytical frameworks to optimize collections outcomes.

Data Management & Insights

• Analyze large structured and unstructured datasets to uncover actionable insights.
• Conduct exploratory data analysis (EDA) and data quality assessments.
• Collaborate with data engineering teams to prepare and maintain analytical datasets.
• Create dashboards, reports, and presentations to communicate findings and recommendations.

Stakeholder & Client Engagement

• Work directly with client and operations stakeholders to understand business requirements and translate them into analytical solutions.
• Present analytical findings, model outcomes, and business recommendations to technical and non-technical audiences.
• Partner with collections, operations, and technology teams to drive implementation of analytical solutions.
• Provide consulting support on collections strategy optimization using data-driven approaches.

Required Skills & Qualifications

Technical Skills

• 7+ years of experience in Data Science, Predictive Analytics, or Machine Learning.
• Strong expertise in building and deploying predictive/propensity models.
• Hands-on experience with:
• Python (Pandas, NumPy, Scikit-learn, XGBoost, LightGBM)
• SQL
• Machine Learning algorithms
• Statistical Analysis and Predictive Modeling
• Strong experience in defining the strategy to measure business benefits post solution deployment.
• Experience working with large-scale datasets and data mining techniques.
• Strong understanding of model evaluation metrics and validation methodologies.
• Experience with cloud platforms such as Azure, AWS, or GCP is preferred.
• Familiarity with MLOps and model deployment frameworks is an advantage.

Domain Experience

• Experience in Collections Analytics, Credit Risk, Financial Services, Banking, Automotive Finance, or Customer Analytics is highly preferred.
• Understanding of customer lifecycle analytics, debt collection strategies, and propensity modeling use cases.

Communication & Consulting Skills

• Excellent verbal and written communication skills.
• Strong client-facing experience and stakeholder management capabilities.
• Ability to simplify complex analytical concepts for business audiences.
• Experience working in global and cross-functional teams.

Preferred Qualifications

• Master's degree or Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field.
• Experience with lending analytics is a plus

Key Skills

Education

Bachelor's or Master's degrees