Data Scientist - HOS Analytics

Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together. Alignment Healthcare is a data and technology driven healthcare company focused on partnering with health systems, health plans, and provider groups to provide care delivery that is preventive, convenient, coordinated, and that results in improved clinical outcomes for seniors. We are seeking a mission-driven Data Scientist to join our growing Stars team, with a primary focus on HOS (Health Outcomes Survey) — the functional health and outcomes survey component of our Star Ratings strategy. This role is responsible for modeling member-level physical and mental health trajectories, identifying the clinical and operational drivers behind HOS measure performance, and translating those insights into targeted interventions that move specific Star Rating cut points. This is a unique opportunity to work at the intersection of healthcare, clinical outcomes, and survey methodology — directly shaping the strategy behind one of the most influential and least understood components of the Star Ratings program. GENERAL DUTIES/RESPONSIBILITIES: Collaborate with Stars, clinical, and care management leaders to understand HOS performance drivers and translate them into analytical solutions. Segment members by likelihood to respond to the HOS survey and by predicted physical or mental health trajectory, to prioritize outreach and intervention. Build and fine-tune models predicting member-level risk of HOS composite decline, including Improving/Maintaining Physical Health, Improving/Maintaining Mental Health, Monitoring Physical Activity, and Reducing the Risk of Falling. Analyze item-level HOS survey data, not just composite scores, to isolate which specific survey questions are driving measure performance. Build pipelines that track HOS survey administration cycles — baseline and follow-up cohorts, sampling, fielding, and response rates — and validate case-mix adjustment logic against CMS methodology. Model statistical significance and year-over-year variance to distinguish genuine performance shifts from survey noise or sampling error. Coordinate with HOS survey vendors on cohort tracking, sampling methodology, fielding timelines, and case-mix adjustment specifications. Collaborate with engineering teams to version, test, and deploy models using Git, CI/CD pipelines, and virtual machine (VM) environments. Partner with clinical, care management, complex case management, Compliance, and Legal teams to ensure HOS-related data outputs and interventions align with CMS guidance. Build dashboards tracking measure-level HOS performance against Star Rating cut points and prior-year trend for each composite. Help standardize definitions, documentation logic, and reporting workflows to scale HOS analytics enterprise-wide. Supervisory responsibilities: N/A Minimum Requirements: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Experience: Minimum Experience: 2+ years of relevant experience in predictive modeling and analysis, with demonstrated application to longitudinal or outcomes-based member data. Education: • Required: PhD in Computer Science, Engineering, Mathematics, Statistics, or related field ​ Domain Expertise (Required) Demonstrated experience with CMS HOS survey methodology, including baseline/follow-up cohort design, case-mix adjustment, and item response theory or other psychometric methods. Working knowledge of individual HOS composite measures: Improving/Maintaining Physical Health, Improving/Maintaining Mental Health, Monitoring Physical Activity, and Reducing the Risk of Falling. Working knowledge of CMS Star Ratings procedures, including how HOS measures are weighted, cut-point determined, and incorporated into the overall Star Rating. Other: Excellent communication, analytical, and collaborative problem-solving skills. Experience building end-to-end data science solutions and applying machine learning methods to real-world problems with measurable outcomes. Solid data structures and algorithms background. Strong programming skills in one of the following: Python, Java, R, Scala, or C++. Demonstrated proficiency in SQL and relational databases. Experience with data visualization and presentation, turning complex longitudinal outcomes analysis into clear, actionable insight for non-technical stakeholders. Experience setting experimental or analytical frameworks for complex, ambiguous scenarios, including longitudinal/cohort study design. Understanding of relevant statistical measures such as confidence intervals, significance of error measurement, and development/evaluation data sets. Experience manipulating and analyzing complex, high-volume, high-dimensionality, and unstructured data from varying sources. Preferred Qualifications: Healthcare experience, particularly within Medicare Advantage. Experience with functional status, frailty, or geriatric outcomes modeling. Experience in cloud ecosystems such as Azure or AWS. Published work in academic conferences or industry circles related to survey methodology, psychometrics, or health outcomes