Head of Data Science

Key Responsibilities

Leadership & Strategy

• Lead and mentor 200250+ member data science, ML engineering, and analytics organization
• Define enterprise AI/ML vision, roadmap, and best practices
• Drive AI adoption across products, platforms, and business units
• Partner with CXOs, product heads, and business stakeholders

Hands-on AI / ML

• Stay hands-on with model design, feature engineering, evaluation, and deployment
• Build and review solutions involving:
• Predictive & prescriptive analytics
• NLP, Computer Vision, Deep Learning
• MLOps and model lifecycle management
• Ensure models are production-ready, scalable, and explainable

Databricks & Platform Engineering

• Architect and implement AI/ML pipelines on Databricks (Spark, MLflow, Delta Lake)
• Optimize large-scale data processing and model training
• Collaborate with cloud and data engineering teams

Middleware & Integration

• Work closely with middleware teams to:
• Integrate ML services via APIs / microservices
• Enable real-time & batch model consumption
• Ensure secure, scalable communication between applications, data platforms, and AI services
• Understand middleware tools (Kafka, API Gateways, ESB, message queues, etc.)

Healthcare / Insurance Focus

• Apply AI to use cases such as:
• Claims processing & fraud detection
• Risk scoring & underwriting
• Patient/member analytics
• Operational optimization & compliance

Software Development Practices

• Embed AI into enterprise software development lifecycle (SDLC)
• Promote Agile, CI/CD, DevOps, and MLOps practices
• Ensure high code quality, documentation, and governance