Data Analyst

ParetoHealth•Philadelphia, PA

17d•Hybrid

About The Position

This role is designed for an experienced, analytically sophisticated individual contributor who feels most at home in the space between data science and traditional BI analytics. As a Data Analyst, you will partner closely with Data Scientists and cross-functional stakeholders to tackle complex, ambiguous analytical problems, focusing on the operationalization and maintenance of solutions. You will contribute to analyses end-to-end, with a focus on long-term sustainability and enterprise-wide utilization of solutions. This role is not focused on building predictive models or machine learning systems for production, and it does not include people management responsibilities. Success in this role comes from analytic rigor, a deep interest in efficiency and standardization, and the ability to turn complex data into decisions. You will report to the VP of Analytics.

Requirements

A problem-solver with a natural sense of curiosity, analytic rigor, and a deep interest in understanding complex healthcare data.
A collaborator with proven ability to work cross-functionally across many different stakeholders to create and deliver insights.
A strong communicator who effectively shares information at the appropriate level across a wide variety of audiences.
A self-starter with a demonstrated track record of owning projects from idea to execution.
A critical thinker with demonstrated ability to be creative, strategic, and to apply strong business judgement.
A translator who can turn complex analysis into actionable insights and recommendations for the business.
3-5+ years’ experience in a healthcare-based analytics role, preferably in the health benefits space/insurance industry, with a solid understanding of the US based health insurance system.
Hands-on experience working with administrative healthcare data (medical and pharmacy claims).
Strong mathematical and statistical foundations, including familiarity with quantitative reasoning, distributions, rates, and variability as applied to real-world healthcare data.
Strong SQL skills, including building, optimizing, and validating complex analytical queries.
Proficiency in data visualization tools such as Tableau.
Experience using Python for analysis and data wrangling.
Experience in data cleaning, validation, and exploratory analysis using large, complex datasets.
Understanding of analytical methods, including descriptive statistics, cohort analysis, risk assessment, trend analysis, and causal thinking.
Strong analytic judgment: knowing which method to use, why, and when.
Bachelor’s degree in a quantitative/analytic field, Master’s preferred.

Nice To Haves

Deep knowledge of Python (advanced modeling not required).
Experience working with data from CRM systems, e.g., Salesforce.
Familiarity with underwriting and actuarial risk assessments for healthcare insurance coverage.
Familiarity with AWS Serverless Services and data tools, such as S3, Lambda, Glue, Sagemaker and EMR.

Responsibilities

Support End-to-End Analyses: Partner with Lead Analysts/Data Scientists to understand business priorities and translate them into analytical approaches.
Design, test, and refine appropriate methodologies for complex analytical questions with an eye toward long term sustainability.
Work directly with large, messy healthcare datasets, including medical and pharmacy claims.
Build and maintain robust SQL queries and analytical datasets.
Deliver Decision-Ready Insights: Develop and support clear visualizations and summaries that communicate findings to technical and non-technical audiences.
Translate complex analyses into actionable recommendations in partnership with Analytics teammates.
Document methodologies, assumptions, and limitations to ensure transparency and repeatability.
Collaborate Within the Team: Work closely with Data Scientists and Lead Analysts to support exploratory analyses, hypothesis generation, and interpretation of results.
Partner with Analytics teammates to align definitions, methodologies, and data quality standards.
Contribute through collaboration and shared problem-solving.
Drive Business Impact: Support stakeholder-identified opportunities where analytics can improve business outcomes.
Support teams across the organization, including underwriting, product, account management, and cost containment.