Senior Data Scientist

GSK•Upper Merion Township, PA

About The Position

The Manufacturing Science and Technology (MSAT) organization is a site-based technical team responsible for ensuring manufacturing processes are capable, compliant, and productive while maintaining product quality. The site MSAT team also drives continuous improvement of the manufacturing supply chain supported by UM Biopharm and collaborates with global MSAT to execute technology transfers and implement process-related improvement programs across the product lifecycle. The Upper Merion MSAT team is also accountable for delivering advanced data science, artificial intelligence, and analytics solutions to improve operational performance, accelerate knowledge generation, and support data-driven decision making across manufacturing and technical development. As a Data Scientist within MSAT, you will play a key role in designing, developing, and deploying data science and AI/ML solutions that enhance manufacturing operations, accelerate technology transfer, and improve product and process understanding. You will leverage advanced analytics, machine learning, and statistical modeling to extract insights from complex manufacturing, process, and quality datasets. This role requires strong collaboration with cross-functional teams including MSAT, Manufacturing, R&D, Engineering, Quality, and Digital/Tech organizations to identify opportunities where advanced analytics and AI can drive measurable business outcomes. You will translate business problems into technical solutions and support the deployment of scalable digital products that improve operational efficiency and knowledge management across the site.

Requirements

BS in engineering or data/computer science, AI/ML, data science and related disciplines, biopharmaceutical development and/or manufacturing with 4+ years of post-graduate experience in a data science role (BS) or 2+ years (Masters) or (PhD).
Expertise in multivariate data analysis (MVDA) and statistical modeling methods including PCA, PLS, latent variable modeling, multivariate statistical process control (MSPC), and design of experiments (DOE).
Experience applying machine learning techniques including regression, classification, clustering, anomaly detection, and time-series modeling to complex datasets.
Experience with data science and statistical analysis tools such as Python, R, JMP, and SIMCA for advanced analytics, statistical modeling, and multivariate process analysis.
Experience with cloud-based and industrial analytics platforms such as Databricks and Seeq, including analysis of large-scale manufacturing and time-series process data.
Experience developing full-stack data and analytics applications using Python and modern web frameworks (e.g., Flask, React, or similar technologies) to build interactive analytics tools and dashboards.

Nice To Haves

PhD in engineering or computer science, AI/ML, data science and related disciplines, biopharmaceutical development and/or manufacturing.
Strong verbal and written communication skills particularly in technical and regulatory related matters. Able to present data science / statistical methods to non-statistical team members.
Able to interact well with peers, subordinates, and senior personnel in scientific, quality, engineering and operational disciplines. Embraces a team-based culture.
Knowledge of GMPs and major regulatory agency regulations, specifically as they relate to data science or data validation.

Responsibilities

Lead the development and deployment of advanced data science, statistical modeling, and machine learning solutions to support manufacturing process optimization, technology transfer, and process monitoring across the product lifecycle.
Develop predictive, diagnostic, and prescriptive analytics models to analyze complex manufacturing, process, and quality datasets and generate actionable insights.
Build and maintain scalable data pipelines and data engineering workflows to integrate data from manufacturing systems (e.g., MES, historians, LIMS, process sensors, and quality systems).
Design and develop digital tools, dashboards, and visualization platforms to enable real-time monitoring and data-driven decision making.
Apply advanced analytics and multivariate data analysis to improve process understanding, identify root causes of variability, and drive continuous improvement initiatives.
Collaborate with cross-functional stakeholders (MSAT, Production, Engineering, Quality, R&D, and Digital/Tech teams) to translate business needs into technical solutions and define product requirements.
Own the lifecycle of digital products, including problem definition, model development, validation, deployment, change management, and user adoption.
Ensure compliance with GSK digital, data integrity, and Tech Quality Risk and Compliance policies, including model governance and validation practices appropriate for regulated environments.
Evaluate and introduce emerging technologies including new machine learning frameworks, modeling platforms, and process analytical technologies to enhance manufacturing capabilities.
Promote data-driven culture by supporting training, knowledge sharing, and adoption of advanced analytics across the site.