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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As a Senior Associate - Data Science & Research on the Morgan Health team, you will query, compile, manipulate, and synthesize large volumes of health-related data, including medical and pharmacy claims, survey results, and biometrics. You will design creative solutions and novel methodologies to help business partners make data-driven decisions within the employer-sponsored insurance industry. Your daily work involves translating business needs into analytics projects, automating processes, and ensuring data integrity while adhering to HIPAA guidelines. You will collaborate with engineers to build high-quality code and data transformations using Python and SQL. The role requires proficiency in R, ggplot, seaborn, and Tableau, alongside expertise in applied statistics like controlled regressions and propensity-score matching. You will also produce research briefs and communicate complex analytical insights to non-technical stakeholders to solve problems regarding the commercially insured population.
Translate business needs into data-driven analytics projects and automate processes where applicable.
Clean, join, and analyze large volumes of health-related data while adhering to HIPAA guidelines.
Identify and investigate diverse data sources including insurance claims, biometrics, and survey results.
Develop high-quality code for data architecture and transformations using Python and SQL.
Propose novel research questions and methodologies to address employer-sponsored insurance challenges.
Create externally-facing research briefs and papers to showcase insights to the healthcare industry.
Validate analysis findings to ensure data quality and integrity across all projects.
Communicate complex analytics concepts and results to non-technical stakeholders and teammates.
What we're looking for
Bachelor’s or Master's degree in a quantitative field such as statistics, engineering, math, analytics, computer science, public health, operations research, or economics.
4+ years of professional experience in hands-on analytics work including relational database structures, data wrangling, and coding languages like SQL, Python, or R.
Proficiency with analytical and data visualization tools such as ggplot, seaborn, or Tableau.
Understanding of applied statistics, including controlled regressions and propensity-score matching.
Excellent verbal and written communication skills to deliver analytical results to non-technical stakeholders.
Experience in healthcare, specifically within a payer or provider setting (preferred).
Experience with medical/pharmaceutical claims data, survey research methods, or machine learning techniques (preferred).
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