Job Type: Full-time
Description
Data Scientist
About Us
Navvis is a leading population health company, driving performance in value-based care. As an operating partner to some of the country’s most innovative health systems, physician enterprises, and health plans, we provide solutions that accelerate the journey to value-based care. Our approach is market-based – we respect the unique needs of populations in each community, including access to care, culture, values, and capabilities. Together with our partners, we set a new national standard in healthcare performance that delivers the affordability, quality, access, and experience that all patients deserve.
Learn more at: www.navvishealthcare.com
Department Overview
What if we routinely asked every person involved in providing or receiving care: “What matters to you and why?” How would understanding “what matters” enhance our ability to transform health in communities and strengthen the connective process, leading to deeper levels of interaction and integration? At NAME OF DEPARTMENT, we are deeply passionate about understanding what matters to people to ensure the delivery of Real‑Person Care. We create an ecosystem that builds a foundation for better physical, social, and emotional health. Our Care Solutions, Analytics, Implementations, Clinical and Business Operations, and Learning and Development teams work tirelessly in partnership with our clients and stakeholders in communities to address the real‑life healthcare needs people have every day.
As a Data Scientist You Will
* Design and implement rigorous program evaluation studies using observational healthcare data.
* Develop analytic frameworks to measure outcomes such as cost reduction, quality improvement, and patient engagement.
* Apply causal inference methods, including PSM, DiD, IPTW, RDD, and other quasi‑experimental approaches.
* Develop and validate statistical models to estimate treatment effects and assess program impact.
* Integrate machine learning algorithms (e.g., random forests, gradient boosting, neural networks) into predictive models using claims, EHR, SDOH, and clinical registry data.
* Work with structured and unstructured healthcare data to engineer features from demographics, clinical history, utilization patterns, costs, and SDOH.
* Build and refine predictive models for risk stratification and disease progression using longitudinal and time‑to‑event analysis.
* Collaborate with clinical, operational, and data engineering teams to embed models into workflows and develop scalable, reproducible data pipelines.
* Maintain transparent, governed, reproducible analytic workflows, including version‑controlled code and documentation.
* Contribute to best practices and methodological standards for program evaluation across the organization.
* Stay current with advancements in causal inference, ML, and healthcare analytics to strengthen evaluation rigor.
A Day In The Life
* Running causal inference analyses (e.g., matching, weighting, DiD) and performing sensitivity checks like parallel trends, placebo tests, and falsification strategies.
* Exploring and preparing healthcare datasets (claims, EHR, SDOH) and engineering features for modeling and evaluation.
* Estimating heterogeneous treatment effects to identify patient subgroups most likely to benefit from interventions.
* Developing predictive models for risk stratification, hospitalization risk, or high‑cost event prediction.
* Reviewing model performance, refining assumptions, and troubleshooting data or methodological issues.
* Collaborating with clinicians, operational leads, or care‑management partners to interpret findings and translate them into actionable insights.
* Creating visual analyses, dashboards, or presentations to communicate findings to both technical and non‑technical audiences.
* Collaborating with data engineers to create scalable, reproducible, and high‑quality data pipelines supporting evaluation and outcomes measurement.
* Documenting analytic decisions, version‑controlled code, and completing peer reviews of methodological work.
What Success Looks Like In This Role
* Program evaluations that are methodologically rigorous, transparent, reproducible, and trusted by clinical and executive stakeholders.
* Predictive models that meaningfully improve risk identification, intervention targeting, care management efficiency, and patient outcomes.
* Clear, compelling communication of insights that drive decision‑making and operational changes.
* Strong partnerships with clinical, operational, analytics, and engineering teams resulting in models and evaluations that are adopted and actively used.
* High‑quality data pipelines and analytic workflows that are scalable, governed, and easy to maintain.
* Continuous enhancement of evaluation and modeling approaches through adoption of emerging techniques and industry best practices.
* Enterprise‑level dashboards and reporting tools that clearly demonstrate program performance and organizational impact.
* Identification of meaningful heterogeneity in treatment effects that helps target interventions to the right patient populations.
Requirements
We are excited about you if you have these things:
* Master’s or Ph.D. in Statistics, Biostatistics, Epidemiology, Health Economics, Data Science, or a related field.
* 3+ years of experience in healthcare analytics, program evaluation, or applied statistics.
* Proficiency in statistical programming languages such as R or Python.
* Experience with healthcare data (claims, EMR, registry data, etc.) and high familiarity with data privacy regulations (e.g., HIPAA).
* Strong understanding of causal inference methods and their application to real‑world data.
* Experience with predictive modeling for risk stratification and disease progression in healthcare settings.
* Experience with VBC analytics, risk adjustment, health equity measurement, and population health management.
* Excellent communication skills and ability to translate complex analyses, assumptions, and study limitations into actionable insights for technical and non‑technical audiences.
* Experience and passion for AI and automation.
Preferred Qualifications
* Experience with heterogeneous treatment effect estimation (causal forests, meta‑learners, uplift modeling).
* Knowledge of sensitivity testing frameworks (robustness checks, placebo/falsification tests, parallel trends verification).
* Familiarity with longitudinal or panel data modeling (fixed effects, random effects, mixed models).
* Familiarity with machine learning frameworks and longitudinal/panel data modeling for disease trajectory analysis.
* Advanced data visualization skills (Tableau, Power BI).
* Understanding of healthcare quality measures, risk adjustment, and population health management frameworks.
* Demonstrated ability to influence program or policy decisions through data‑driven insights and impact measurement.
What you’ll get:
Navvis is committed to attracting the most insightful and motivated talent by providing a candidate and onboarding experience that you won’t find elsewhere! We foster an environment and culture that allow people to be creative, feel connected and be inspired to do their best work no matter where they are on the map. For all colleagues at Navvis, we strive to ensure that they have everything needed to be successful. From the basics like a competitive total rewards strategy, volunteering and social engagement activities to creating company experiences that challenge you to think differently and do different things as part of our never‑stop learning ecosystem, we support the whole person when you become a team member at Navvis. Navvis offers a competitive benefits package including medical, dental, vision, 401(k) with a safe‑harbor contribution, and a paid time‑off plan starting at 2+ weeks.
Our Commitment:
Navvis is an equal employment opportunity and affirmative action employer seeking diversity in qualified applicants for employment. All applicants will receive consideration for employment without regard to race, ethnicity, color, gender, gender identity, age, religion, creed, national origin, ancestry, disability, perceived disability, medical condition, genetic information, military or veteran status, sexual orientation, or any other protected status, as defined by applicable law. Prior to the next step in the recruiting process, we welcome you to inform us confidentially if you may require any special accommodation to complete your application and participate fully in our recruitment experience. Contact us at H.R@navvishealthcare.com.
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