Lead Data Scientist

Lead Data Scientist - Credit risk

Experience: 7+ Years

Job location: Chennai - Work From Office(5 Days)

Shift Timings: 1:00 PM-10:00 PM IST

Role & responsibilities

Mentor and grow a small India team of data scientists and analysts; instill best

practices in model development, validation, and governance.

• Partner with U.S. leadership to shape the credit analytics roadmap and

prioritization.

• Lead discussions with business and partner teams to define analytical requirements

and problem statements.

Risk Modeling & Analytics (Technical Core)

• Build, validate, and monitor credit risk scoring models including Probability of default (PD), Loss given Default (LGD), and Exposure at Default(EAD) frameworks.
• Develop fraud detection, early delinquency prediction, and portfolio stress-testing models.

• Design scenario analysis and simulation models to assess portfolio resiliency under different market or credit conditions.

• Partner with Data Engineering to ensure robust, scalable, and high-quality data pipelines.

Underwriting & Decision Systems

• Collaborate with Product and Risk teams to integrate models into loan decisioning systems.
• Use external bureau, bank transaction, and alternative data to enhance model accuracy and decision transparency.
• Ensure explainability, interpretability, and regulatory compliance across all models.
• Work consultatively with lending partners or external data providers to evaluate and integrate credit innovations. Consulting & Stakeholder Engagement
• Act as a strategic advisor to business leaders on how analytics can drive growth, optimize risk, and improve customer experience.

• Lead data-driven storytelling sessions and synthesize complex model results into clear business narratives.

• Represent the data science function in cross-functional or external partner meetings

Preferred candidate profile

Technical Skills

• Deep expertise in credit risk modeling (e.g., logistic regression, decision trees,

survival analysis, scorecards, machine learning for risk).

• Strong programming skills in Python and SQL; experience with MLOps tools,

version control (Git), and cloud systems (AWS/GCP).

• Knowledge of bureau, bank transaction, and alternative data sources.
• Familiarity with model monitoring, governance, and explainability frameworks.

Experience

• 712 years in data science or analytics, with at least 45 years in credit risk / financial services / fintech / NBFC / banking.

• Proven experience deploying credit models into production environments.

• Prior leadership or mentoring experience in analytics or data science teams.

• Strong stakeholder and client-handling experience—preferably involving consulting, business analytics delivery, or partner-facing projects.

• Experience in multi-geography organizations is highly desirable.