Job Description
Job Title:
Lead - Data Science
Posting Start Date:
10/06/2026
Job Description:
The Data Scientist will play a key role in driving analytical innovation for healthcare claims analytics and KOL (Key Opinion Leader) networking intelligence initiatives. This role will contribute to the design, development, and deployment of advanced analytical models, patient journey analytics, HCP/HCO segmentation, and network analytics solutions to support commercial, medical affairs, and market access strategies.
The position involves close collaboration with cross-functional teams—including data engineering, product, commercial analytics, and business stakeholders—to translate complex business questions into scalable analytical and machine learning solutions.
The ideal candidate has hands-on experience in Python scripting, healthcare claims data analytics, and ML model development, along with exposure to cloud ecosystems such as Azure and AWS. Knowledge of healthcare data models, provider affiliation/network analysis, and graph-based analytics is highly preferred. This role requires strong problem-solving skills, analytical rigor, and the ability to communicate complex insights clearly to both technical and business audiences.
Responsibilities:
Develop, implement, and maintain machine learning models and analytical solutions focused on healthcare claims analytics, patient journeys, HCP targeting, and KOL networking insights.
Perform exploratory data analysis, feature engineering, and model evaluation using large-scale healthcare datasets including claims, provider, affiliation, and engagement data.
Analyze complex patient and provider-level datasets to identify treatment patterns, referral behaviors, prescribing trends, and influential HCP/KOL networks.
Support development of advanced analytics use cases such as HCP segmentation, influence scoring, peer network analysis, referral mapping, and engagement optimization.
Collaborate with Data Engineering teams to ensure healthcare data pipelines and analytical workflows are optimized for scale, quality, and reliability.
Work within Azure Databricks / AWS cloud environments to operationalize ML workflows, automate scoring pipelines, and support scalable analytics delivery.
Apply statistical and machine learning techniques to generate actionable insights supporting commercial, medical affairs, and market access decisions.
Create clear documentation for analytical methodologies, feature definitions, business rules, and model outputs to ensure reproducibility and governance.
Communicate analytical findings, risks, assumptions, and business impact effectively to technical and non-technical stakeholders.
Apply best practices in model validation, testing, monitoring, and continuous improvement for healthcare analytics solutions.
Ensure all analytical processes comply with healthcare data governance, privacy, security, and compliance standards.
Contribute to the definition of technical and functional requirements for future claims analytics and KOL intelligence capabilities.
About You (Desired Profile):
3–5+ years of experience in Python scripting, healthcare data analytics, and applied machine learning.
Strong proficiency in Python (pandas, numpy, scikit-learn) and SQL.
Hands-on experience working with healthcare claims data, provider/HCP datasets, patient-level longitudinal data, or pharmaceutical commercial analytics datasets.
Understanding of healthcare concepts such as patient journeys, treatment pathways, HCP affiliations, referral networks, and commercial targeting analytics.
Experience with exploratory data analysis, feature engineering, statistical modeling, and ML model validation.
Familiarity with graph/network analytics, influence modeling, or KOL identification methodologies is preferred.
Working knowledge of Azure (preferably Data Factory and Databricks) or AWS cloud platforms for large-scale data processing and ML workflows.
Ability to translate business requirements into analytical frameworks and scalable machine learning solutions.
Strong communication and storytelling skills to present insights and recommendations to business stakeholders.
Experience with ML lifecycle management, versioning, and model governance tools is a plus.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Bioinformatics, or related quantitative field.
Experience working within pharmaceutical, healthcare, or life sciences analytics environments is strongly preferred.
Certifications in Azure or AWS data/ML technologies are desirable.