Lead Data Scientist

Location: Chicago, United States

Department: Data Science

Experience: 10 - 15 years

• 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).

• 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.

• 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.

• 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).