ROLE OVERVIEW:

The Head of Data Science will serve as the executive technical leader responsible for building, scaling, and operationalizing next-generation data infrastructure, machine learning execution engines, and enterprise AI platform capabilities.

This high-impact role brings together Data Engineering, ML Engineering, MLOps, LLMOps / Agentic AI Infrastructure, Enterprise Decisioning Platforms, and SRE / DataOps into a cohesive, production-grade engineering organization.

The leader will be accountable for translating business and technology strategy into high-throughput, low-latency, scalable, and audit-ready platform solutions that power real-time credit decisioning, risk modeling, dynamic pricing, fraud prevention, collections, and portfolio management across millions of active accounts.

Key Responsibilities:

1. Engineering Leadership:

- Build, scale, and mentor Data Engineering, ML Engineering, MLOps, and AI Platform engineering teams while establishing engineering best practices and operational rigor across squads.

2. Enterprise Decisioning Platform:

- Design, operationalize, and scale a centralized decisioning platform integrating low-code model development, AutoML, real-time rule engines, policy-as-code, real-time scoring, and automated workflow execution.

3. Data Platform Architecture:

- Lead the strategic vision and implementation of scalable, cloud-native Lakehouse architectures using technologies such as Databricks, Delta Lake, Unity Catalog, DLT, and Spark for high-volume batch and real-time streaming workloads.

4. MLOps & LLMOps Standardization:

- Establish standardized, production-grade MLOps frameworks along with scalable LLM and Agentic AI infrastructure.

5. Production Deployment & Scaling:

- Partner closely with Data Science teams to enable seamless and automated transition of machine learning and deep learning models from experimentation to highly available production environments.

6. Operational Excellence & SRE:

- Lead DataOps and SRE functions to ensure high platform availability, proactive automated testing, self-healing systems, and continuous CI / CD delivery.

7. Model & Data Governance:

- Operationalize comprehensive model lifecycle governance and regulatory compliance frameworks aligned with applicable financial-services regulations, data-protection requirements, and internal risk policies.

8. Observability & System Health:

- Establish end-to-end telemetry and monitoring.

Candidate Requirements:

- 15 - 22 years of total experience.

- 8+ years in senior management - platform leadership.

- Only from Top-tier education - IIT / IISc / BITS / NIT / IIIT.

- Strong Data Engineering & Data Platform Architecture.

- Deep MLOps / ML Platform experience.

- AI / LLMOps / Agentic AI infrastructure.

- Cloud & Infrastructure - AWS / GCP, Kubernetes, Docker, IaC.

- Large-scale engineering leadership.

- Fintech / NBFC / Banking + lending / credit / risk / fraud exposure.

- Real-time decisioning + production-grade platform experience.

- BCA / MCA candidates will not be considered.