Data Scientist, Health Economics

Remote (US)

Foodsmart

With the largest national network of registered dietitians, we've helped over 1.5 million members improve their health with personalized nutrition guidance from the comfort of their own home.

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About us:
Foodsmart is the leading telenutrition and foodcare solution, backed by a robust network of Registered Dietitians. Our platform is designed to foster healthier food choices, drive lasting behavior change, and deliver long-term health outcomes. Through our highly personalized, digital platform, we guide our 2.2 million members—including those in employer-sponsored health plans, regional and national Medicaid managed care organizations, Medicare Advantage plans, and commercial insurers—on a tailored journey to eating well while saving time and money.
Foodsmart seamlessly integrates dietary assessments and nutrition counseling with online food ordering and cost-effective meal planning for the entire family, optimizing ingredients both at home and on the go. We partner with national and regional retailers across the U.S., many of whom accept SNAP/EBT, making healthier food more accessible. Additionally, we assist members with SNAP enrollment and management, providing tangible access to nutritious food.In 2024, Foodsmart secured a $200 million investment from TPG’s Rise Fund, which supports entrepreneurs dedicated to achieving the United Nations’ Sustainable Development Goals. This investment will help us expand our reach, particularly to low-income workers who are disproportionately affected by diet-related diseases. 
At Foodsmart, our mission is to make nutritious food accessible and affordable for everyone, regardless of economic status. We are committed to a set of core values that shape our culture and work environment:
Measured: We make data-driven, truth-seeking decisions.Impactful: We are fueled by achieving our mission and vision.Collaborative: We help each other be better and create a positive environment.Hungry: We maintain a healthy growth mindset, seeking to overcome challenges with courage.Joyful: We take joy in each other, our work, and the privilege of doing this work.
Whether you're a dietitian, a commercial leader, or a technologist, working at Foodsmart means being part of a team that is passionate, supportive, and driven by a shared purpose. Join us in transforming the way people access and enjoy healthy food.
About the role:
We are looking for a self-driven Data Scientist to provide insights, predictive modeling, and build the infrastructure for our Health Economics function. You are passionate about improving people’s lives on a massive scale through insights from large datasets and developing best practices for health plans and health systems. You have experience working with large claims, conducting cost effectiveness/ROI analyses, conducting statistical analyses, building machine learning models, simulation models, and interpreting results.

You will:

  • Work closely with the Head of Clinical Data Science to support advanced statistical analyses and interpretation
  • Use a statistical software package (R, Python, etc) to run statistical analyses on healthcare utilization, engagement data, and claims cost analyses
  • ​​Build out the ontology and standardized data model for claims data
  • Explore using statistical learning/machine learning/bayesian methods to predict cost savings, ER/inpatient utilization, and run simulation models to evaluate step therapies and disease progression from claims data
  • Write codes to make quantitative associations (using regression analyses and causal inference methods) 
  • Developing and advancing methodologies to evaluate cost effectiveness and cost savings from claims data
  • Assist in automation of codes/analytic processes
  • Clean data and conduct quality checks 
  • Create and interpret data dictionaries, specifications, and other technical documentation share by or with internal and external stakeholders

You are:

  • Outcome-driven problem solver: You are passionate about applying statistical and machine learning techniques to solve complex healthcare challenges, with a keen focus on delivering data-driven results.
  • Analytical and decisive: You make data-informed decisions under pressure, balancing detailed analysis with sound judgment to pursue high-value initiatives while maintaining focus on project objectives.
  • Adaptable and resourceful: You thrive in dynamic environments, finding creative, technically sound solutions under tight constraints without sacrificing data integrity or organizational values.
  • Skilled collaborator: You excel at working cross-functionally, translating technical insights into actionable strategies for both technical and non-technical stakeholders to drive impactful outcomes.

You have:

  • 2-6 years of experience in with Healthcare Claims (Medical, Rx) and Eligibility Files
  • At least 2-4 years of experience with machine learning, causal inference; bayesian statistics and simulation modeling (in particular MCMC) is a plus
  • Expert in R (tidyverse, caret, xgboost), Python (numpy, pandas, scikit-learn, statsmodel, SciPy), or similar statistical software package, and SQL (window functions, CTE’s, aggregate functions, etc.)
  • Experience with claims analyses and with large, real-world datasets; not limited to merging datasets and cleaning raw data
  • Undergraduate or Masters in biostatistics, epidemiology, statistics, data science, computer science, health economics, or equivalent degree 
  • Public health background is a plus!
About our benefits and perks:
Remote-First CompanyUnlimited PTOFlexible & remote location (NYC Area preferred)Healthcare Coverage (Medical, Dental, Vision)401k, bonus, & stock optionsCommuter benefit Gym reimbursement
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Category: Data Science Jobs

Tags: Bayesian Biostatistics Causal inference Computer Science Economics Excel Machine Learning ML models NumPy Pandas Predictive modeling Python R Scikit-learn SciPy SQL Statistics XGBoost

Perks/benefits: Career development Health care

Regions: Remote/Anywhere North America
Country: United States

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