Data Science Director - Remote

The Cigna Group•Bloomfield, CT

2d•$178,500 - $297,500•Remote

About The Position

Help shape how healthcare risk decisions are made. At The Cigna Group, pricing and underwriting decisions rely on data-driven insights that are accurate, explainable, and operationally reliable. As the Data Science Director for Pricing & Underwriting, you will lead high-impact teams that build and evolve machine learning models influencing business growth, risk selection, forecasting accuracy, and underwriting effectiveness. This is an opportunity to combine technical excellence, strategic leadership, and business partnership to deliver meaningful outcomes across a complex healthcare environment.

Requirements

10+ years of experience in data science, advanced analytics, statistical modeling, machine learning, actuarial analytics, or a related quantitative field.
5+ years of experience leading and developing technical teams responsible for production-grade analytics or machine learning solutions.
Strong expertise in machine learning, statistical modeling, model monitoring, validation, and explainability techniques.
Hands-on experience with Python, SQL, Git, and modern analytics development practices.
Experience managing model lifecycle processes, including governance, documentation, monitoring, testing, and production support.
Demonstrated ability to influence senior business and technical stakeholders and translate complex analytical findings into actionable business decisions.

Nice To Haves

Experience with healthcare payer data, claims analytics, pricing, underwriting, risk adjustment, actuarial analytics, or other risk-focused domains.
Experience with Databricks, Spark, cloud-based analytics platforms, or large-scale data environments.
Advanced degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Actuarial Science, or a related quantitative discipline.

Responsibilities

Lead the strategy, development, enhancement, and support of machine learning models that enable pricing, underwriting, risk scoring, presale, and renewal decision-making.
Establish and continuously improve model lifecycle practices, including development, validation, monitoring, documentation, governance, release management, and model refresh processes.
Provide technical leadership across machine learning, statistical modeling, feature engineering, model evaluation, calibration, explainability, and production-ready analytics.
Drive execution across multiple model products by prioritizing work, managing delivery plans, coordinating releases, and ensuring reliable production support.
Partner with pricing, underwriting, actuarial, sales, finance, technology, data engineering, and governance teams to embed model outputs into business processes and decision frameworks.
Translate complex analytical concepts into clear business insights, helping stakeholders understand model performance, limitations, opportunities, and risks.
Ensure compliance with model governance, audit, documentation, and monitoring requirements while maintaining high standards for quality and operational reliability.
Build and develop a high-performing data science organization through coaching, talent development, collaboration, accountability, and continuous improvement.