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# Data Scientist

- **Company:** Insight Global
- **Location:** Newark, NJ, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Information Engineering, Integrated Development Environments, Python (Programming Language), Machine Learning, SQL Databases, Feature Engineering, Random Forest, Model Validation, Generative AI, Data Analytics, Xgboost, Data Pipelines
- **Published:** June 29, 2026
- **Apply:** https://www.juju.com/job/00000000gcbs8g

## About the Role

We are seeking an experienced Data Scientist to join our clients Life Insurance Underwriting team. This role will support large-scale underwriting initiatives focused on mortality risk modeling and premium determination. The ideal candidate brings strong traditional data science experience, a solid foundation in statistical and machine learning techniques, and the ability to work independently in a production, business-facing environment. This is not a data engineering or GenAI-heavy role. The majority of the work will focus on classical machine learning and statistical modeling using structured health and insurance data., Strong experience in traditional data science and machine learning

Proficiency in Python and SQL

Hands-on experience with:

 * Regression models

 * Random Forest

 * Gradient boosting methods (e.g., XGBoost)

Experience working in a Git/GitHub-based development environment

Familiarity with standard data science workflows and best practices

Masters or PhD in a Data Science related field Experience in insurance, life insurance underwriting, healthcare, or health data analytics

Exposure to actuarial concepts or risk modeling

Limited exposure to AI / GenAI tools (expected usage ~10-15%, not a core requirement)

## Description

Develop, validate, and maintain machine learning and statistical models to support life insurance underwriting decisions

Analyze and model health and demographic data to assess mortality risk and premium outcomes

Translate underwriting and business requirements into analytical solutions

Partner with underwriters, analysts, and stakeholders to interpret model outputs and findings

Conduct exploratory data analysis, feature engineering, and model performance evaluation

Document methodologies, assumptions, and results in a clear and structured manner

Contribute to model lifecycle activities including monitoring and refinement


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