Morgan Health - Health Care Innovation - Senior Associate of Data Science & Research

JPMorgan Chase & Co.•New York, NY

33d

About The Position

In 2021, JPMorgan Chase launched Morgan Health, a new business unit focused on improving the quality, equity, and affordability of employer-sponsored health care in the United States. Morgan Health pursues this strategy through investments, collaborations with the JPMC Benefits team, engagements with other market leaders, sophisticated data analytics and research, and policy advocacy. Morgan Health is headquartered in Washington, DC, with members of the team also based in New York City and Boston. To learn more about our strategy and latest developments, please visit: www.morganhealth.com. As a Senior Associate of Data Science & Research within Morgan Health, you will utilize their background in statistics, programming, and health care data analytics to support initiatives across Morgan Health. The ideal candidate will be passionate about healthcare transformation and empowering data-driven decisions across the industry. You will be responsible for querying, compiling, manipulating, and synthesizing large quantities of health-related data - including medical and pharmacy claims, survey data, and biometrics. You will design creative solutions and novel methodologies that enable Morgan Health and its business partners to bring new insights to employer-sponsored insurance. Beyond strong analytics and communication skills, the ideal candidate will be a self-motivated problem-solver who enjoys working in teams and creates value by aligning data-driven insights with business objectives.

Requirements

Bachelor's or Master's degree in a quantitative field including, but not limited to statistics, engineering, math, analytics, computer science, or economics
4+ years of professional experience in hands-on analytics work, including building and leveraging relational database structures, object-oriented coding, data wrangling, data visualization, and code versioning tools (e.g., Python, R; ggplot, seaborn, Tableau; git)
Strong critical reasoning and coding skills, with a problem-solving mindset that anticipates edge cases, questions assumptions, and designs creative solutions that account for nuances in the data
Experience working with real-world health care data to produce clear analytical insights
Experience in applied statistics, including controlled regressions and hypothesis testing
Proven ability to successfully multitask effectively and deliver accurate results with limited supervision
Excellent verbal communication skills and experience creating analytics presentations for both technical and non-technical stakeholders with a strong attention to detail
Team-oriented attitude and proven collaborator

Nice To Haves

Master's degree or PhD with a focus on health care analytics, health services research, data science, statistics, or engineering
Health care specific experience in a payer, provider, or research setting that actively used medical and pharmaceutical claims data, including standard groupers, common data transformations, and population health metrics
Experience applying more advanced applied statistical concepts, including propensity score matching, difference-in-differences, instrumental variables, and other causal inference methods in a real-world setting
Experience working with unstructured data and/or machine learning techniques
Extensive content knowledge of U.S. healthcare system, including understanding common business questions about the commercially insured population, value-based care, and point solutions serving the employer-sponsored insurance market
Peer-reviewed research outputs in fields including applied statistics, health care analytics, informatics, and medicine

Responsibilities

Translate business and research questions from stakeholders across Morgan Health into data-driven analytics approaches that balance precision with timeliness
Proactively identify relevant data sources (including insurance claims, biometrics results, survey results, public or licensed data) and understand their context and limitations
Implement creative and efficient solutions to clean, join, and analyze data within Python and SQL while adhering to HIPAA and firm-wide data use guidelines
Collaborate with other data scientists and engineers to ensure code is well-documented, high-quality, efficient, scalable, and automated (when applicable)
Carefully review and validate analysis findings and participate in team code reviews to ensure data quality and integrity
Propose novel research questions or methodologies that solve questions relevant to employer-sponsored insurance
Compile externally-facing research briefs and papers that showcase insights relevant to other employers and the broader health care industry
Clearly communicate analytics concepts and insights with non-technical teammates and stakeholders

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