Sr. Data Scientist, Specialty Networks Solutions

Cardinal Health, Inc.; Field (NM); On-site; USD 120,000 - 180,000; Full time

Job summary

Cardinal Health, Inc. is seeking a Sr. Data Scientist in Specialty Networks Solutions to lead the design and deployment of real-world data products and advanced analytics for biopharma clients.

You will ensure data quality, optimize availability, and drive measurable commercial and clinical value across healthcare data ecosystems. The role spans clinical informatics, data engineering, and client strategy, requiring expertise in EHRs, claims, lab data, and genomic data, with a strong engineering

Qualifications

• 8+ years of data science, advanced analytics, healthcare data, or real-world evidence.
• Bachelor's degree in related field, or equivalent work experience.
• Advanced degree in measurement, statistics, epidemiology, or related field.
• Experience with complex healthcare datasets (EHR, claims, lab, genomic data).
• SQL and at least one language (R or Python); Databricks or modern data platforms.
• Understanding of healthcare data models and real-world evidence methodologies.
• Proven ability to identify and resolve data quality and system issues.
• Ability to translate business requirements into technical specifications.

Responsibilities

• Data governance and standards; define high-priority variables and QC rules.
• Harden data assets and data pipelines for production reliability and regulatory defensibility.
• Collaborate with engineering to ensure data delivery meets client requirements.
• Engage with clients to present analyses, methodologies, and strategic recommendations.

Skills

Data science

Advanced analytics

Healthcare data

Real-world evidence

SQL

R/Python

Databricks

Data quality

Technical specification

Education

Bachelor's degree in related field

Advanced degree in measurement/statistics/epidemiology

Tools

Databricks

Job description

Specialty Networks is an integrated, multi-specialty organization with GPOs, Life Sciences, and Research solutions in Urology, Oncology, Gastroenterology, and Rheumatology.

Specialty Networks solutions include population health management, patient engagement, clinical research, and workflow automation technologies to achieve Center of Excellence status in Standards of Care, Clinical Research, Practice Operations, and Value-based Care.

Specialty Networks leverages the Power of its Network, represented by over 10,000 providers, to help independent physician practices, industry, wholesalers/distributors, payers, and policymakers improve patient outcomes.

What Data Science contributes to Cardinal Health

The Data & Analytics Function oversees the analytics lifecycle in order to identify, analyze and present relevant insights that drive business decisions and anticipate opportunities to achieve a competitive advantage. This function manages analytic data platforms, the access, design and implementation of reporting/business intelligence solutions, and the application of advanced quantitative modeling.

Data Science applies base, scientific methodologies from various disciplines, techniques and tools that extracts knowledge and insight from data to solve complex business problems on large data sets, integrating multiple systems.

Job Summary

The Sr. Data Scientist in Specialty Networks Solutions leads the design, hardening, and deployment of high-impact real-world data (RWD) products and advanced analytical solutions for biopharmaceutical partners. Acting at the intersection of clinical informatics, data engineering, and biopharma client strategy, this individual is directly responsible for ensuring clinical data quality, optimizing data availability, and unlocking measurable commercial and clinical value for life sciences clients. The role requires deep domain expertise across healthcare data ecosystems (electronic health records, medical/pharmacy claims, specialty pharmacy dispensing, lab registries), sophisticated quantitative and machine learning capabilities, and a rigorous engineering approach to transforming prototype analyses into resilient, production-grade data products.

Responsibilities

• Data Governance & Standards
  Define governed definitions for the highest-priority derived variables
  Set Quality Control rules for each transformation step in datasets, profile the raw and transformed data hands-on, and trace anomalies to root cause
  Document the pipeline and data model, including the decision logic at each transformation step
  Harden data assets and analytical pipelines to ensure production reliability, regulatory defensibility, and reproducibility across client-facing deliverables
• Engineering Partnership
  Assess engineering delivery meets client and quality requirements, and close gaps (e.g., order data not flowing into deliverables)
  Run a shared request queue for questions from clients and internal analysts, and track each item with engineering
  Identify key growth areas from the market to enhance data pipelines both in structured and unstructured data sources
  Prioritize workflows to deliver more value through product generation
  Accelerate data accessibility by standardizing common data models (e.g., OMOP, clinical registries) and enabling rapid extraction of fit-for-purpose real-world evidence (RWE)
• Client & Strategic Partnership
  Join discovery conversations alongside the delivery team, and build prototype dashboards on real data (e.g., a patient journey) as the specification for engineering
  Facilitate listening sessions with key biopharmaceutical partner clients to understand existing and future challenges.
  Present analytical findings, data methodologies, and strategic recommendations to technical and non-technical audiences, including senior client stakeholders

Qualifications

• 8+ years of data science, advanced analytics, healthcare data, or real-world evidence, including experience supporting biopharmaceutical client, preferred.
• Bachelor's degree in related field, or equivalent work experience, required
• Advanced degree in measurement, statistics, epidemiology, or a related field, preferred
• Demonstrated expertise working with complex healthcare datasets, including electronic health records (EHR), medical and pharmacy claims, specialty pharmacy data, laboratory and genomic data, and patient identity/tokenization solutions such as Datavant.
• Advanced proficiency in SQL and at least one statistical or programming language such as R or Python, with experience working in modern data platforms including Databricks.
• Strong understanding of healthcare data models, clinical concepts, patient journeys, provider and drug mapping, and real-world evidence methodologies.
• Proven experience identifying, investigating, and resolving complex data quality and system-level issues through rigorous analytical and root-cause analysis techniques.
• Experience translating business and client requirements into technical spec