ABOUT US
At Vida, we help people get better- and we're helping the healthcare system get better, too.
Vida is a virtual, personalized obesity care provider that uses evidence-based treatment to help patients manage obesity and related conditions like diabetes, high blood pressure, anxiety and depression. Vida's team of Obesity Medicine-Certified Physicians, Registered Dietitians, Expert Coaches and Licensed Therapists takes a whole-person approach to care, helping people lose weight, reduce stress and improve their overall health.
By combining advanced technology with top-notch healthcare providers, Vida is breaking down the barriers that have historically kept people from getting the best care. It's trusted by Fortune 100 companies, major national payers and large providers to enable their employees to live their healthiest lives.
Vida Health is looking for a Director of Health Economics & Outcomes Research (HEOR) to generate and advance the evidence that tells the value of Vida. Reporting to the SVP of Business Intelligence & Health Analytics, you will be a senior individual contributor who translates the priorities of our growth, strategy, and clinical teams into real-world evidence. The role involves leading peer-reviewed and white-paper publications and turning complex clinical and claims data into the story of Vida's impact for commercial growth, payer strategy, value-based contracting, and clinical credibility.
This role is highly self-directed in execution. Research priorities are set by the business, principally growth, strategy, and clinical teams, and your objective is to translate those priorities and needs into credible, compelling evidence: scoping the right analyses, running them end-to-end, and driving publications with minimal oversight. You will sit at the intersection of rigorous evidence generation and business impact, turning what the business needs to prove into work that is both scientifically sound and commercially consequential.
Responsibilities:
Take the questions that growth, strategy, and clinical teams need answered and design, plan, and execute the health-outcomes analyses that answer them, using biometric, claims, and clinical data for both internal and external audiences.
Apply modern AI tools (e.g., LLM-based assistants) in your day-to-day workflow to accelerate literature review, analysis, code, and manuscript drafting, with demonstrated proficiency, not just familiarity.
Conduct literature reviews, run the analyses, and prepare manuscripts for peer-reviewed journals and clinical white papers, carrying work from concept to submission.
Apply methods such as multilevel/mixed-effects modeling, survival analysis, case-control difference in difference, budget impact analysis, and clustering (e.g., K-means, DBSCAN) to complex, time-series, and nested data structures.
Partner with growth, strategy, and clinical teams to generate the economic and clinical evidence that supports enterprise sales, client retention, and value-based care contracting.
Work fluently with medical and pharmacy claims to quantify utilization, cost, and savings outcomes.
Monitor pipelines for measurement fidelity and quality, and curate and prepare biometric, claims, and clinical data for analysis.
Coordinate evidence and reporting projects across product, marketing, engineering, and business development, providing the project-management structure that keeps multi-stakeholder work on track.
Additional responsibilities as needed.
Qualifications:
Bachelor's degree required.
5-7+ years of hands-on experience in clinical analytics, HEOR, health economics, digital health, or a related domain.
Demonstrated, hands-on experience analyzing healthcare claims data and applying rigorous statistical methods required.
High proficiency in at least one statistical package: Python (preferred) or R; fluency in SQL.
Startup/growth-stage experience; equivalent scrappy operating experience within a larger org also works.
Comfortable sourcing your own data and delivering rigorous work without the infrastructure, resources, or processes of a large organization
Experience with time-series and nested/longitudinal data.
A record of peer-reviewed publications or research projects, ideally in digital health, telemedicine, or value-based care.
Proven ability to take a business question and independently carry it to a published result: scoping, analyzing, and delivering with minimal oversight.
Demonstrated, hands-on proficiency using AI tools (e.g., LLM-based assistants) to do analytical and writing work more efficiently and to a higher standard.
Ability to translate technical findings into clear, persuasive insight for non-technical executives, commercial, and external audiences.
Familiarity with HIPAA and data-privacy requirements as they relate to PHI and digital health.
Preferred:
Master's or PhD in Public Health, Epidemiology, Biostatistics, Statistics, Psychology, Health Economics, or a domain pertinent to digital health/telemedicine highly preferred.
Experience with end-to-end data processing pipelines, from ingestion through advanced analytics.
Applied data science/machine learning experience.
Familiarity with modern analytics tooling: Jupyter, Google Colab, Git, Jira, Confluence, Google Cloud Platform.
Experience in value-based contracting or payer-facing evidence generation.
Experience collaborating with multidisciplinary teams in a fast-moving tech environment.
