Job Summary
We are seeking a highly skilled
Senior Data Scientist with expertise in
Predictive Analytics, Machine Learning, Explainable AI, and Azure AI Services to develop next-generation predictive models and AI-powered analytical solutions. The ideal candidate will have strong experience in
healthcare, insurance, or benefits analytics, with the ability to transform large-scale enterprise data into actionable business insights.
This role will focus on building predictive utilization models, designing explainable AI solutions, and developing conversational AI agents using Azure technologies to enable business users to interact with enterprise data through natural language.
Key Responsibilities
- Design, develop, and validate predictive models for Baseline Utilization Forecasting using historical claims, member demographics, plan design, diagnosis, and procedure data.
- Build attribution and explainability frameworks to identify key drivers behind differences between expected and actual utilization.
- Perform advanced feature engineering on structured healthcare and insurance datasets stored in SQL Data Warehouse, Azure Data Fabric, OneLake, and Databricks.
- Develop and optimize statistical, machine learning, and time-series forecasting models to improve prediction accuracy.
- Build and validate actuarial-style frequency and severity models using:
- Poisson Regression
- Negative Binomial Models
- Gamma Regression
- Tweedie Models
- Conduct model validation, back-testing, and performance monitoring against historical outcomes and existing forecasting methods.
- Implement Explainable AI (XAI) techniques such as SHAP values, feature importance, and driver attribution to provide transparent model insights for business stakeholders.
- Collaborate closely with Data Engineering teams to ensure high-quality, production-ready datasets and semantic consistency across analytical platforms.
- Work extensively within Azure Data Fabric, including OneLake, Lakehouse, Fabric Data Science, and Domino platform.
- Integrate predictive models with Power BI semantic models and enterprise reporting solutions.
- Design and develop AI-powered agents and copilots using Azure OpenAI and LLM technologies for conversational analytics and intelligent insight generation.
- Support deployment, monitoring, and lifecycle management of machine learning models following MLOps best practices.
Required Skills & Qualifications- 5–8+ years of experience in Data Science, Machine Learning, or Advanced Analytics.
- Strong domain expertise in Healthcare, Group Insurance, Health Benefits, or Claims Analytics.
- Excellent understanding of utilization analytics and healthcare business drivers.
- Strong programming skills in Python and SQL.
- Hands-on experience with:
- Machine Learning
- Statistical Modeling
- Predictive Analytics
- Time-Series Forecasting
- Experience building explainable AI solutions using SHAP or similar techniques.
- Experience developing AI Agents, Copilots, or LLM-powered applications using Azure OpenAI or equivalent platforms.
- Experience working with Azure cloud analytics ecosystem.
- Strong communication and stakeholder management skills with the ability to present complex analytical findings to business leaders.
Preferred Skills
- Azure Machine Learning
- Microsoft Fabric
- Azure Data Fabric
- OneLake
- Databricks
- Domino Data Science Platform
- Power BI Semantic Models
- MLOps and Model Governance
- Feature Stores
- Responsible AI
- Azure OpenAI Service
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- AI Agent Frameworks
- Healthcare Claims Data Modeling
Technical Skills
- Python
- SQL
- Azure Data Fabric
- Microsoft Fabric
- Azure Machine Learning
- Databricks
- OneLake
- Power BI
- Azure OpenAI
- LLMs
- SHAP
- Scikit-learn
- Pandas
- NumPy
- XGBoost / LightGBM
- Time-Series Forecasting
- Poisson Regression
- Negative Binomial Models
- Gamma Regression
- Tweedie Models
- MLOps
- Git
- Azure DevOps
Preferred Industry Experience
- Healthcare
- Health Insurance
- Group Benefits
- Managed Care
- Payer Analytics
- Claims Analytics
- Actuarial Analytics
Education
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related quantitative discipline.
Skills: data science,azure,ai,machine learning
