Data Science Director - Remote

The Cigna Group•Remote,

1d•$178,500 - $297,500•Remote

About The Position

The Data Science Director for Pricing & Underwriting at The Cigna Group leads teams developing and evolving machine learning models that inform pricing, underwriting, risk selection, forecasting, and business decisions across a complex healthcare environment. The role combines technical leadership, strategic direction, and business partnership to support growth, risk selection, forecasting accuracy, and underwriting effectiveness. The Director sets model strategy and oversees the full model lifecycle, including development, validation, monitoring, documentation, governance, release management, and refresh processes. They guide technical work across machine learning, statistical modeling, feature engineering, evaluation, calibration, explainability, and production analytics, while coordinating delivery and dependable production support across multiple model products. The position partners with pricing, underwriting, actuarial, sales, finance, technology, data engineering, and governance teams to integrate model outputs into business processes. It also communicates analytical findings and model limitations clearly to stakeholders and develops a high-performing data science organization through coaching, talent development, collaboration, accountability, and continuous improvement. The role is remote; occasional or permanent work from home requires cable broadband or fiber internet with at least 10 Mbps download and 5 Mbps upload.

Requirements

At least 10 years of experience in data science, advanced analytics, statistical modeling, machine learning, actuarial analytics, or a related quantitative field.
At least 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 strategy, development, enhancement, and support of machine learning models for pricing, underwriting, risk scoring, presale, and renewal decisions.
Establish and improve model lifecycle practices, including development, validation, monitoring, documentation, governance, release management, and model refresh.
Provide technical leadership in machine learning, statistical modeling, feature engineering, model evaluation, calibration, explainability, and production-ready analytics.
Prioritize and manage delivery plans across multiple model products, coordinate releases, and ensure reliable production support.
Partner with pricing, underwriting, actuarial, sales, finance, technology, data engineering, and governance teams to embed model outputs in business processes and decision frameworks.
Translate complex analytical concepts into clear business insights about model performance, limitations, opportunities, and risks.
Ensure compliance with model governance, audit, documentation, and monitoring requirements and maintain quality and operational reliability.
Build and develop a high-performing data science organization through coaching, talent development, collaboration, accountability, and continuous improvement.