Job Description

Qualifications & Talents:

• Bachelor’s/master’s in computer science, Statistics, Data Science, or related field. 
• 10+ years in ML/Data Science, with 3+ years leading GenAI projects in insurance/finance.
• Expertise in Python, ML libraries (Pandas, NumPy, scikit-learn, TensorFlow, PyTorch), and GenAI frameworks (LangChain, LangGraph).
• Strong stats, algorithms, data structures; experience with large datasets, visualization (Matplotlib, Seaborn, Tableau).
• Excellent communication, problem-solving, and team leadership skills.
• Passion for AI innovation and insurance domain knowledge (e.g., binding authority, actuarial models).

We are seeking a seasoned Lead Data Scientist to spearhead AI and machine learning initiatives for our insurance operations. You will design, develop, and deploy advanced GenAI solutions like RAG and Agentic AI workflows to optimize risk assessment, claims automation, fraud detection, and personalized underwriting. Leading a team, you'll integrate AI into production systems on Cloud platforms, drive model performance, and align innovations with business goals in the dynamic insurance landscape.

  Key Responsibilities:

 Generative AI & Agentic Workflows:

• Design and implement RAG systems and Agentic AI workflows using prompt engineering, fine-tuning of LLMs, and frameworks like LangGraph/LangChain to automate insurance processes such as policy binding and claims adjudication.
• Develop autonomous AI agents for tasks like real-time risk scoring and customer query resolution.
• Evaluate LLMs for accuracy, bias mitigation, and alignment with insurance regulations (e.g., IRDAI compliance).

  Model Development & Deployment:

• Architect, build, and refine ML/GenAI models (traditional and generative) to tackle insurance challenges like predictive analytics for market risk, anomaly detection in claims, and isolation forests for fraud.
• Deploy scalable models in production on AWS/Azure/GCP.
• Optimize models using performance metrics, feedback loops, and A/B testing for cost-efficiency and reliability.

  Leadership & Collaboration:

• Lead cross-functional teams to integrate AI into existing workflows, enhancing efficiency in underwriting, binding authority, and operations.
• Develop robust benchmarks, evaluation metrics, and monitor model drift/bias in large insurance datasets.
• Stay ahead of AI advancements, mentoring juniors and presenting insights to stakeholders.

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