Lead Data Scientist

Chicago, IL - USA

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

Qualifications & Talents:

Bachelors/masters 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 youll 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.

Qualifications & Talents:Bachelors/masters 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...

Qualifications & Talents:

Bachelors/masters 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).

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.