Senior Commercial Data Science Manager

GSK•Philadelphia, PA

About The Position

GSK’s Data & Analytics team integrates, simplifies, and unlocks data to drive innovation and decision-making and better serve patients, healthcare professionals, and consumers. The Senior Commercial Data Science Manager leads a team of 2–4 data scientists and engineers and delivers analytics and insights for the US commercial oncology business. The role applies AI/ML and statistical models to identify business learnings at the patient, HCP, and health system levels, supporting analytical priorities such as customer segmentation, brand performance reporting, predictive patient modeling, propensity scoring, sentiment analysis, marketing mix modeling, omnichannel analytics, resource allocation and budget optimization, and experimental design analyses. The role develops, deploys, maintains, and monitors analytics automation in enterprise data platforms within a CI/CD framework. It partners with commercial reporting owners to develop agent-based AI tools for analytics automation, insight delivery and visualization, and business-focused data interrogation. The manager identifies data science opportunities to uncover patient unmet needs and HCP prescribing patterns, contributes to GSK’s data science community and best practices, and builds data science literacy across the organization. The position establishes project plans and delivers milestones, builds trusted stakeholder partnerships to support data-driven decisions, and collaborates across functions to enable efficient decision-making. It uses storytelling and visualizations to communicate evidence-based recommendations and encourage the use of data science in business decisions.

Requirements

Master’s or PhD in statistics, mathematics, engineering, or a related analytical field.
At least 5 years of experience applying analytics and statistical skills in a business setting.
At least 3 years of professional experience with analytics tools or languages and visualization tools.
Experience with analytics tools and languages such as SQL, Python, and PySpark, and with visualization tools.
Experience working in cloud compute environments and with Databricks or Snowflake data platforms.
Experience coding statistical and machine learning models.
Experience developing and deploying AI agentic processes.
Ability to proactively identify areas for analysis, including potential applications of machine learning, and contribute to problem-solving.

Nice To Haves

Enthusiasm for leveraging real-world data.
Analytical curiosity and ability to develop innovative solutions to complex problems.
Effective communication skills and strong business acumen.
Understanding of business needs and ability to develop appropriate solutions.
Teamwork skills and ability to build strong collaborative partnerships.
Familiarity with healthcare datasets, such as claims data from IQVIA or Komodo Health.
Experience modeling treatment lines of therapy, analyzing patient journeys, and prioritizing HCPs.

Responsibilities

Manage a team of 2–4 data scientists and engineers.
Develop and deliver analytics and insights using AI/ML and statistical models for the US commercial oncology business.
Support analytical priorities including customer segmentation, brand performance reporting, predictive patient modeling, propensity score modeling, sentiment analysis, marketing mix modeling, omnichannel analytics, resource allocation and budget optimization, and experimental design analyses.
Develop, deploy, maintain, and monitor analytics automation in enterprise data platforms within a CI/CD framework.
Work with commercial business reporting owners to develop agent-based AI tools for analytics automation, insight delivery and visualization, and business-focused interrogation of data.
Identify data science opportunities to uncover drivers of patient unmet needs and HCP prescribing patterns and improve strategic opportunities.
Contribute to GSK’s data science community, best practices, data skills, and organizational data science literacy.
Engage stakeholder partners and use analytics expertise, learning, and business knowledge to support data-driven decisions.
Establish project plans, seek required inputs, and deliver results and milestones according to plan.
Collaborate across matrix teams and functions to drive efficiencies and seamless decision-making.
Use storytelling and visualizations to communicate data science findings and fact-based recommendations to business partners and analytics team members.