Responsibilities

• Develop and maintain the testing roadmap, determining which outreach and engagement techniques are evaluated and in what order.
• Design controlled experiments, including hypothesis definition, randomization approach, control group construction, sample sizing, and success criteria.
• Conduct power analysis and establish minimum detectable effects prior to each test launch.
• Analyze test results and determine whether observed differences are statistically valid, accounting for sequential analysis and multiple comparisons.
• Analyze historical appointment, outreach, and attendance data to identify the drivers of missed visits and quantify the addressable opportunity.
• Apply observational causal inference methods where controlled testing is not feasible, and clearly communicate the limitations of those methods.
• Develop segmentation and targeting approaches to determine which outreach methods are most effective for which patient populations.
• Translate findings into clear recommendations and implementation specifications for the operations team.
• Establish and maintain documentation of all tests, including design, results, and resulting decisions.
• Report show rate performance and program impact to leadership, including operational and financial impact.

Requirements

• Bachelor’s degree in statistics, economics, mathematics, data science, marketing analytics, or a related field, or equivalent practical experience.
• 5+ years of experience in marketing science, experimentation, causal inference, or advanced analytics.
• Demonstrated experience designing, executing, and analyzing controlled experiments end to end.
• Working knowledge of experimental design principles, including randomization, sample sizing, power analysis, stratification, and multiple comparisons.
• Advanced SQL, plus proficiency in Python or R.
• Experience working with large, imperfect operational datasets.
• Experience applying observational causal inference methods such as matching, difference-in-differences, or propensity scoring.
• Strong written and verbal communication skills, including the ability to present analytical findings to non-technical audiences.
• Sound judgment in distinguishing statistically valid results from inconclusive ones, and the ability to communicate that distinction clearly.

Preferred

• Healthcare experience is a plus but is not required for this role.
• Experience in healthcare, including provider, payer, digital health, or pharmacy settings.
• Experience with patient or member engagement programs, such as Medicare Advantage, Stars, HEDIS, or medication adherence.
• Experience developing uplift or heterogeneous treatment effect models.
• Experience designing and running experiments in a regulated environment.
• Working knowledge of HIPAA and TCPA as they apply to patient communications.
• Familiarity with behavioral science as applied to consumer or patient behavior.
• Experience working with older adult populations.
• Advanced degree in a quantitative discipline.