Sr. Data Scientist - Remote

UnitedHealth Group•Minnetonka, MN

2d•$91,700 - $163,700•Remote

About The Position

This 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. GenAct is a de-prioritization model that drives personalized suppression and actional conversation outcomes for phone channels. MART is comprehensive statistical based testing framework. In partnership with cross-functional stakeholders, the role will strengthen testing, monitoring, governance, and data integrity practices to enable scalable and compliant deployment of next best action capabilities aligned to enterprise goals. Government Programs Marketing and the Role of Offer Analytics: Government Programs Marketing supports Medicare, DSNP, Medicaid, MedSupp, IFP, and related member populations by creating relevant, compliant, and personalized engagement experiences across digital, telephonic, and emerging channels. The organization’s marketing priorities depend on understanding members’ needs, delivering the right message at the right time, improving digital adoption, supporting Stars and clinical outcomes, and enabling sustainable growth and retention through modernized MarTech capabilities. Within this environment, the Offer Analytics team serves as a critical analytical and decisioning partner. The team helps transform offer strategy from static business rules into a more member-centric, value-based, and measurable capability. By combining offer eligibility, member behavior, channel engagement, business value, model scoring, and test design, the team enables prioritization and personalization approaches that balance member relevance with enterprise objectives. Advance intelligent offer prioritization through OPTiN, helping determine which eligible offers or next best actions should be surfaced first across member touchpoints Support personalization at scale by integrating member-level data, offer attributes, channel behavior, interaction history, and business value into analytical decisioning frameworks Enable stronger measurement and test discipline through MART, including A/B testing, holdout design, readout standards, and success metrics that connect engagement outcomes to business impact Improve suppression and de-prioritization strategies through GenAct and related analytics, reducing repetitive or low-value outreach while improving actionable conversation outcomes Partner with offer owners, marketing leaders, product, data, engineering, legal, compliance, and channel teams to ensure offer strategies are operationally feasible, analytically sound, and aligned to regulatory expectations Provide insights on offer performance, eligibility shifts, member engagement, channel attribution, and optimization opportunities so Government Programs Marketing can make informed decisions about where to scale, adjust, or retire offer strategies

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
Experience establishing data quality controls, source-to-output validation checks, and monitoring practices that support reliable, scalable, and compliant analytical solutions
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
Knowledge of machine learning lifecycle practices, including feature development, model validation, performance monitoring, model drift, data drift, bias, fairness, and responsible AI considerations
Proven ability to partner effectively with cross-functional teams, including marketing, product, data engineering, legal, compliance, technology, channel operations, and business stakeholders
Proven solid communication and storytelling skills, with the ability to translate complex analytical findings into clear recommendations, dashboards, readouts, and executive-ready insights

Nice To Haves

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

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
Drive expansion of 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
Support the development of MART, a comprehensive statistical based A/B testing framework, to drive prioritization optimization efforts. This initiative aims to improve next best action performance, decision quality, and deliver business outcomes via OPTiN model
Develop data science-driven offer performance and journey insights reporting, including dashboards and analytical outputs that surface trends, measure impact, identify optimization opportunities, and inform stakeholder decision-making
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
Contributes 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