Data Science Engineer

Position Overview

Zelis is a leading payments company in healthcare, guiding, pricing, explaining, and paying for care on behalf of insurers and their members.

We align the interests of payers, providers, and consumers to deliver a better financial experience and more affordable, transparent care for all.

Partnering with 700+ payers, supporting 4 million+ providers and 100 million members across the healthcare industry.

Zelis Data Intelligence (ZDI) is a centralized data team that partners across Zelis business units to unlock the value of data through intelligence and AI solutions. Our mission is to transform data into a strategic and competitive asset by fostering collaboration and innovation.

Enable the democratization and productization of data assets to drive insights and decision-making.

Develop new data and product capabilities through advanced analytics and AI-driven solutions.

Collaborate closely with business units and enterprise functions to maximize the impact of data.

Leverage intelligence solutions to unlock efficiency, transparency, and value across the organization.

Key Responsibilities

Conduct comprehensive data analysis using statistical and exploratory methods to uncover patterns and insights that drive data-driven decision-making in the US healthcare domain.

Work with large datasets, including healthcare business unit (BU)-specific data such as claims, eligibility, provider networks, patient demographics, payments, and utilization trends.

Leverage knowledge of healthcare industry metrics (e.g., HEDIS, CMS Star Ratings, risk adjustment models, and revenue cycle data) to optimize analytics strategies.

Collaborate with data science and engineering teams to ensure data quality, availability, and reliability for AI / ML-driven healthcare analytics solutions.

Design and maintain data pipelines for efficient ingestion, transformation, and storage of claims, electronic health records (EHR), HL7 / FHIR data, and real-world evidence (RWE).

Ensure compliance with HIPAA, PHI, and other regulatory requirements when handling healthcare datasets.

Develop and maintain dashboards and reports that translate complex healthcare data into actionable insights for business stakeholders.

Use visualization tools such as Streamlit over Snowflake, Power BI, or similar platforms to represent key healthcare metrics, trends, and performance indicators.

Apply expertise in healthcare cost, quality, and operational performance analytics to deliver meaningful insights.

Work closely with cross-functional teams, including data science, engineering, API development, and healthcare operations, to understand data needs and deliver tailored solutions.

Partner with healthcare payers, providers, and revenue cycle management teams to enhance data quality and ensure alignment with industry standards.

Actively engage with Data Science, Data Engineering, and Business Units to enhance process understanding and ensure data accuracy for regulatory and business reporting.

Maintain a proactive mindset in exploring new analytical techniques, regulatory changes, and healthcare industry trends.

Engage with industry experts, attend relevant healthcare and data science conferences, and contribute to continuous learning within the team.

Qualifications

3-5 years of hands-on experience in data analysis, preferably within the US healthcare domain, with exposure to payer, provider, claims, and financial data analytics.

Strong proficiency in SQL and Python, including libraries such as pandas for data manipulation and analysis.

Experience with healthcare data visualization and storytelling using tools such as Streamlit, Snowflake, Power BI, Tableau, or similar.

Familiarity with ETL pipelines, data warehousing, and cloud platforms (AWS, Azure, GCP) for healthcare data processing.

Deep understanding of US healthcare data, including claims, payments, eligibility, patient encounters, and provider networks.

Strong knowledge of healthcare standards and regulations (HIPAA, PHI, HL7, FHIR, CMS, Medicare / Medicaid reporting, NCQA, HEDIS, and risk adjustment models).

Experience in revenue cycle management (RCM), medical coding (ICD, CPT, DRG), and healthcare cost / utilization analytics is a plus.

Ability to analyze complex healthcare datasets and derive meaningful insights that impact operational efficiency, patient outcomes, and cost optimization.

Experience working with predictive modeling and AI-driven healthcare analytics is an advantage.

Excellent communication and stakeholder management skills, with the ability to translate technical findings into business insights.

Strong collaboration skills to work effectively with healthcare business teams, IT, and data science professionals.

A curious mindset with a willingness to explore new challenges and drive innovation in healthcare analytics.