Data Science Director - Remote

Cigna Healthcare•Raleigh, NC

1d•$178,500 - $297,500•Remote

About The Position

The Cigna Group is hiring a Data Science Director for Pricing & Underwriting to lead teams developing and evolving machine learning models that support pricing, underwriting, risk selection, forecasting, and business decision-making. The role combines technical leadership, strategic direction, and partnership across a complex healthcare environment, with a focus on accurate, explainable, and operationally reliable analytics. The director leads model strategy and delivery, establishes and improves model lifecycle practices, and provides technical leadership in machine learning, statistical modeling, feature engineering, evaluation, calibration, explainability, and production analytics. The role coordinates multiple model products and partners with business, actuarial, technology, data engineering, and governance teams to integrate model outputs into business processes. It also translates analytical findings for stakeholders, ensures governance and operational standards, and develops a high-performing data science organization. The position is remote. If working from home occasionally or permanently, the employee must have cable broadband or fiber internet with speeds of at least 10 Mbps download and 5 Mbps upload. The anticipated annual salary is $178,500–$297,500, depending on relevant factors such as experience and geographic location. The position is also anticipated to be eligible for an annual bonus and long-term incentive plan.

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.
An 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 for pricing, underwriting, risk scoring, presale, and renewal decisions.
Establish and continuously improve model lifecycle practices, including development, validation, monitoring, documentation, governance, release management, and model refresh processes.
Provide technical leadership in 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 so stakeholders understand model performance, limitations, opportunities, and risks.
Ensure compliance with model governance, audit, documentation, and monitoring requirements while maintaining quality and operational reliability.
Build and develop a high-performing data science organization through coaching, talent development, collaboration, accountability, and continuous improvement.