Data Scientist – HOS Analytics

Alignment Health

. Collaborate with Stars, clinical, and care management leaders to understand HOS performance drivers and translate them into analytical solutions .

Posted 9/22/2026full-timeRemote • United StatesJuniorMid-Level💰 $149,882 - $224,823 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in predictive modeling and analysis, particularly in healthcare outcomes, with a strong foundation in statistical methods and data science. Proficient in building analytical solutions that align with CMS guidelines and improve HOS performance metrics.

Highest-signal resume keywords

Predictive ModelingCMS HOS Survey MethodologyPython ProgrammingSQL ProficiencyData Visualization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingStatistical AnalysisMachine LearningData StructuresAlgorithmsSQLPythonJavaRC++

Soft Skills

Analytical SkillsCommunication SkillsCollaborative Problem-Solving

Tools & Technologies

GitCI/CD PipelinesAzureAWS

Certifications & Qualifications

PhD in Computer SciencePhD in EngineeringPhD in MathematicsPhD in Statistics

Industry Keywords

HealthcareMedicare AdvantageHOS Composite MeasuresStar RatingsCohort DesignCase-Mix AdjustmentPsychometrics

Tech Stack

Tools & technologies

AWSAzureCloudJavaPythonScalaSQLC++

About the role

Key responsibilities & impact

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 predicted physical or mental health trajectory to prioritize outreach and intervention
Build and fine-tune models predicting member-level risk of HOS composite decline
Analyze item-level HOS survey data to identify survey questions driving measure performance
Build pipelines tracking HOS survey administration cycles, cohorts, sampling, fielding, and response rates
Validate case-mix adjustment logic against CMS methodology
Model statistical significance and year-over-year variance to distinguish 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 VM environments
Partner with clinical, care management, Compliance, and Legal teams to align outputs and interventions with CMS guidance
Build dashboards tracking measure-level HOS performance against Star Rating cut points and prior-year trends
Standardize definitions, documentation logic, and reporting workflows for enterprise-wide HOS analytics

Requirements

What you’ll need

2+ years of relevant experience in predictive modeling and analysis, with demonstrated application to longitudinal or outcomes-based member data
Required: PhD in Computer Science, Engineering, Mathematics, Statistics, or related field
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
Strong programming skills in one of: Python, Java, R, Scala, or C++
Demonstrated proficiency in SQL and relational databases
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
Experience with data visualization and presentation
Experience setting experimental or analytical frameworks for complex, ambiguous scenarios, including longitudinal/cohort study design
Understanding of 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
Excellent communication, analytical, and collaborative problem-solving skills
Preferred: Healthcare experience, particularly within Medicare Advantage
Preferred: Experience with functional status, frailty, or geriatric outcomes modeling
Preferred: Experience in cloud ecosystems such as Azure or AWS
Preferred: Published work related to survey methodology, psychometrics, or health outcomes measurement
Preferred: Track record of handling ambiguity, prioritizing needs, and delivering results in an agile, dynamic environment