Sr. Data Scientist - Remote

About the role

The role is for a data scientist needed to advance OPTiN prioritization and personalization efforts across multiple channels. The position will help design and support AI-driven decisioning approaches that improve the relevance, effectiveness, and responsible use of recommendations for members and the business through contributions to GenAct and MART.

Responsibilities

• Support the design, governance, and ongoing advancement of OPTiN prioritization and personalization approaches for next best actions, helping deliver AI-driven recommendations that are relevant, responsible, and aligned to business and consumer needs
• Expand OPTiN prioritization efforts for primarily offers and other next best actions across additional channels (App, Chat, Dashboard Care Hub) and lines of business (DSNP, MedSupp, IFP), ensuring approaches can be adapted to varying consumer journeys, business needs, and operational requirements
• Identify and mitigate risks related to data integrity, model performance, bias, fairness, model drifts, data drifts, and potential PII/PHI exposure
• Develop MART, a comprehensive statistical based A/B testing framework, to drive prioritization optimization efforts, improving next best action performance, decision quality, and deliver business outcomes via OPTiN model
• Establish data source quality controls and end-to-end system flow checks to identify and address upstream and downstream issues that could impact model performance, operational reliability, and AI-driven decision quality
• Contribute to the development and enhancement of testing methodologies for both traditional and generative AI use cases, including the GenAct initiative, to support the implementation of personalized suppression strategies across all active channels and promote more actionable conversational outcomes for phone channels
• Partner with legal, compliance, data, engineering, product, and AI teams to validate test plans, results, and governance requirements
• Stay current on emerging AI, machine learning, and healthcare regulatory developments and translate them into practical governance and testing approaches

Requirements

• 3+ years of experience in data science, advanced analytics, machine learning, or applied statistics, including hands-on experience developing, evaluating, and improving predictive or prescriptive models in complex enterprise environments
• Experience working with large, complex healthcare, marketing, member, or consumer data sets to generate insights, build analytical solutions, and support data-driven decisioning
• Experience supporting personalization, prioritization, next best action, recommendation, suppression, or decisioning strategies across digital, telephonic, or omnichannel environments
• Solid proficiency with analytical and programming tools such as Python, SQL, R, SAS, Snowflake, Databricks, or similar platforms used for data preparation, modeling, monitoring, and reporting
• Solid understanding of statistical methods, experimental design, A/B testing, holdout testing, hypothesis testing, and performance measurement approaches used to evaluate model and business impact
• Proven ability to partner effectively with cross-functional teams, including marketing, product, data engineering, legal, compliance, technology, channel operations, and business stakeholders
• Poorsonal communication and storytelling skills, with the ability to translate complex analytical findings into clear recommendations, dashboards, readouts, and executive-ready insights

Qualifications

• Experience with healthcare marketing, Medicare, Medicaid, DSNP, MedSupp, IFP, or other regulated member engagement environments
• Experience applying advanced analytics or machine learning to MarTech, CRM, digital engagement, contact center, mobile app, chat, or omnichannel activation use cases
• Experience developing performance dashboards, executive readouts, value measurement frameworks, or business impact analyses that connect analytical outcomes to financial, operational, or member experience results
• Familiarity with offer decisioning, next best action, prioritization, suppression, personalization, recommendation engines, or customer/member journey analytics
• Working knowledge of responsible AI, model governance, regulatory compliance, privacy controls, and risk mitigation practices in environments involving sensitive customer or member data
• Proven ability to influence without direct authority by aligning technical teams, business owners, channel partners, and leadership stakeholders around common decisioning and measurement approaches