Sr. Data Scientist, Specialty Networks Solutions

Cardinal Health

1d•$125,300 - $178,900•Remote

About The Position

The Sr. Data Scientist in Specialty Networks Solutions leads the design, hardening, and deployment of high-impact real-world data products and advanced analytical solutions for biopharmaceutical partners. Working at the intersection of clinical informatics, data engineering, and biopharma client strategy, this role is responsible for clinical data quality, data availability, and creating commercial and clinical value for life sciences clients. The position requires expertise across healthcare data ecosystems—including electronic health records, medical and pharmacy claims, specialty pharmacy dispensing, and lab registries—along with quantitative and machine learning capabilities and an engineering approach to turning prototype analyses into resilient, production-grade data products. The role defines governed variables and quality-control rules, profiles raw and transformed data, traces anomalies to their root causes, and documents data pipelines and models. It partners with engineering to address delivery gaps, improve and prioritize pipelines, standardize common data models, and enable fit-for-purpose real-world evidence extraction. It also participates in client discovery, prototypes dashboards using real data, facilitates conversations with biopharmaceutical partners, and presents findings, methodologies, and recommendations to technical and non-technical stakeholders. This is a remote position based anywhere in the United States. Candidates must be able to work within EST or CST business hours as needed.

Requirements

At least 8 years of experience in data science, advanced analytics, healthcare data, or real-world evidence; experience supporting biopharmaceutical clients is preferred.
Bachelor's degree in a related field or equivalent work experience.
Demonstrated expertise with complex healthcare datasets, including EHR, medical and pharmacy claims, specialty pharmacy, laboratory and genomic data, and patient identity or tokenization solutions such as Datavant.
Advanced proficiency in SQL and at least one statistical or programming language such as R or Python, with experience using 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 ability to identify, investigate, and resolve complex data-quality and system-level issues using rigorous analytical and root-cause analysis techniques.
Experience translating business and client requirements into technical specifications and collaborating with engineering teams to deliver scalable data products.
Demonstrated success influencing stakeholders and driving outcomes in highly matrixed, cross-functional organizations.
Excellent communication and consulting skills, including the ability to present complex analytical concepts to technical and executive audiences.
Ability to work within EST or CST business hours as needed.

Nice To Haves

Experience supporting biopharmaceutical clients.
Advanced degree in measurement, statistics, epidemiology, or a related field.

Responsibilities

Define governed definitions for the highest-priority derived variables.
Set quality-control rules for each dataset transformation, profile raw and transformed data hands-on, and trace anomalies to their root causes.
Document data pipelines and models, including the decision logic used at each transformation step.
Harden data assets and analytical pipelines to support production reliability, regulatory defensibility, and reproducibility across client-facing deliverables.
Assess whether engineering deliveries meet client and quality requirements and close gaps, such as 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 market-driven growth opportunities for data pipelines across structured and unstructured data sources.
Prioritize workflows to generate greater value through product development.
Accelerate data accessibility by standardizing common data models, such as OMOP and clinical registries, and enabling rapid extraction of fit-for-purpose real-world evidence.
Join discovery conversations with the delivery team and build prototype dashboards on real data, such as a patient journey, to serve as engineering specifications.
Facilitate listening sessions with biopharmaceutical partner clients to understand current and future challenges.
Present analytical findings, data methodologies, and strategic recommendations to technical and non-technical audiences, including senior client stakeholders.