Experience: 8+ Years

Employment Type: Full-Time

Role Summary:

We are seeking an experienced Lead Data Scientist with deep expertise in Credit Risk Modeling, Scorecard Development, and Retail Lending Analytics. The ideal candidate will lead the design, development, validation, and deployment of credit risk models and scorecards across the customer lifecycle. The role requires strong analytical capabilities, hands-on experience with statistical modeling techniques, and the ability to partner with business stakeholders to drive data-driven lending decisions and portfolio performance.

Key Responsibilities:

- Lead the development and enhancement of Application Scorecards and Behavioral Scorecards for retail and digital lending portfolios.

- Design, build, and monitor Credit Risk Models including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and IFRS 9 models.

- Develop and implement risk segmentation frameworks to improve credit decisioning and portfolio management.

- Analyze large-scale structured and unstructured datasets to identify risk trends and business opportunities.

- Utilize Credit Bureau data sources such as CIBIL, CRIF, Experian, and Equifax to strengthen underwriting and portfolio strategies.

- Partner with business, risk, product, and technology teams to translate business requirements into analytical solutions.

- Drive model validation, calibration, performance monitoring, and periodic model reviews.

- Ensure adherence to regulatory guidelines, governance frameworks, and model risk management standards.

- Build predictive analytics solutions to optimize customer acquisition, risk assessment, collections, and portfolio profitability.

- Perform data quality assessments and ensure accuracy, completeness, and consistency of analytical outputs.

- Prepare model documentation, validation reports, and audit-ready artifacts.

- Support strategic initiatives through advanced analytics, scenario analysis, and portfolio simulations.

- Mentor and guide junior data scientists, analysts, and model developers.

- Manage stakeholder communication and handle first-level escalations related to analytical solutions and model performance.

Key Result Areas (KRAs):

- Successful development and deployment of high-performing credit risk models and scorecards.

- Improvement in portfolio quality through enhanced risk prediction and underwriting strategies.

- Achievement of model accuracy, stability, and regulatory compliance targets.

- Reduction in model implementation timelines and enhancement of analytical efficiency.

- Effective utilization of bureau and alternative data sources for risk assessment.

- Improved business decision-making through actionable analytical insights.

- Timely completion of model monitoring, validation, and governance activities.

- High-quality model documentation and audit readiness.

- Enhanced customer acquisition and portfolio profitability through data-driven strategies.

- Development of analytical capabilities and mentorship of team members.

- Strong stakeholder satisfaction and effective cross-functional collaboration.

Required Skills & Competencies:

- Expertise in Credit Risk Modeling and Scorecard Development.

- Strong knowledge of PD, LGD, EAD, IFRS 9, Basel Frameworks, and Model Risk Management.

- Hands-on experience with Application Scorecards and Behavioral Scorecards.

- Deep understanding of Retail Lending, Consumer Finance, and Digital Lending businesses.

- Strong experience working with Credit Bureau data including CIBIL, CRIF, Experian, and Equifax.

- Advanced proficiency in Python for statistical modeling and machine learning.

- Strong SQL skills for data extraction, transformation, and analysis.

- Experience with R and/or SAS for model development and validation.

- Knowledge of machine learning algorithms, predictive modeling, and classification techniques.

- Experience in model validation, back-testing, calibration, and performance monitoring.

- Strong understanding of data governance, data quality, and reporting standards.

- Exposure to visualization tools such as Power BI, Tableau, or similar platforms.

- Strong problem-solving and quantitative analytical skills.

- Excellent stakeholder management and communication skills.

- Experience leading teams and managing multiple analytical projects simultaneously.

Preferred Qualifications:

- Master's Degree in Statistics, Mathematics, Economics, Data Science, Computer Science, Engineering, or a related quantitative discipline.

- Experience in Banking, NBFC, FinTech, or Financial Services organizations.

- Exposure to regulatory reporting and risk governance frameworks.

- Experience working with cloud-based analytics environments is an added advantage.