Lead Data Scientist
First seen by Alion on Aug 20, 2026.
Roles & Responsibilities :
- Lead AI Product Pods across Credit Risk, Fraud, and Collections functions.
- Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning.
- Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
- Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines.
- Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization.
- Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards.
- Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes.
- Define AI platform architecture, operational excellence, SLAs, and incident management practices.
- Build, mentor, and scale high-performing AI Engineering and Data Science teams.
Ideal Candidate :
- 1. Strong Lead Data Science, AI Engineer, or Machine Learning Engineer profiles.
- 2. Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.
- 3. Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.
- 4. Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.
- 5. Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.
- 6. Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.
- 7. Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.
- 8. Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.
- 9. Mandatory (Experience 8) - Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.
- 10. Mandatory (Education) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.
- 11. Mandatory (Age) - Candidate's Age should be below 37 years.
- 12. Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
- 13. Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.
- 14. Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.
- 15. Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.
Interview Process :
- 3 Technical Rounds
- Underwriting Call
- Criminal Check
- HR Discussion
Perks & Benefits :
Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of India's most diversified Non-banking financial companies, and among Asia's top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations we're present in India.
Skills
Machine Learning, Python, Artificial Intelligence, Data Science, Data Scientist, Fraud Analytics, SQL, MLOps