Senior Principal Scientist, Oligo Design and Data Science

GSK, Plc.•Cambridge, MA

37d•$121,275 - $202,125•Hybrid

About The Position

GSK is seeking an exceptional and visionary scientist to join the Oligo Design and Data Science group as a Senior Principal Scientist. This pivotal role is at the heart of our strategy to accelerate drug discovery by leveraging massive-scale screening data and machine learning. You will be responsible for architecting and implementing the next generation of our DNA Encoded platforms informatics. As a senior member of the team, you will play a critical role in driving our data strategy and shaping the future of hit-finding at GSK. In this position, you will lead the design and development of critical software infrastructure, from automated ETL pipelines that process terabyte-scale sequencing data to sophisticated web applications and interactive dashboards that enable data-driven decision-making. Your expertise will be instrumental in developing and applying novel statistical methods for analyzing selection data from both small molecule and oligonucleotide libraries, building robust machine learning models to predict structure-activity relationships, and exploring deep learning approaches for hit identification. You will work in a deeply collaborative, cross-functional environment alongside experts in oligo therapeutic design, chemistry, biology, and biophysics to translate complex data into actionable hypotheses that guide our therapeutic discovery programs. The ideal candidate will possess a PhD in computational science and a proven history of building scientific computing platforms from the ground up in a drug discovery setting. Deep expertise in cheminformatics, DNA Encoded Library (DEL) data analysis, and the development of scientific applications using Python (e.g., pandas, scikit-learn, Django), SQL, and modern cloud infrastructure is essential. We are looking for a strategic thinker and a hands-on builder who is passionate about leveraging computation to solve challenging biological problems and is excited by the opportunity to have a significant impact on the future of data-driven drug discovery at GSK.

Requirements

PhD in computational science, bioinformatics, cheminformatics, computer science, or a closely related discipline.
Experience in cheminformatics and DNA-encoded library (DEL) data analysis, including the application of advanced statistical and computational methods to large-scale biological datasets.
Experience developing scientific applications using Python (such as pandas, scikit-learn, Django), SQL, and deploying solutions on modern cloud infrastructure.
On-site presence of 2-3 days per week, as required for team collaboration and project delivery.

Nice To Haves

Experience leading platform development initiatives that integrate research technology, artificial intelligence, and machine learning for scalable data analysis and informatics solutions.
Significant contributions to open-source scientific software projects or recognized achievement in computational life science competitions (e.g., Kaggle, TopCoder, DREAM Challenge).
Expertise in the design and optimization of automated ETL pipelines for processing terabyte-scale sequencing or screening data.
Advanced knowledge of predictive modeling, Bayesian statistics, and deep learning approaches for hit identification and structure-activity relationship prediction.
Demonstrated success in cross-functional communication, matrixed collaboration, and thought leadership within multidisciplinary teams.
Strong analytical and problem-solving skills, with a track record of translating complex biological questions into actionable computational solutions.
Ability to work collaboratively in cross-functional teams, communicating effectively with experts in chemistry, biology, biophysics, and data science.
Experience with analysis of siRNA knockdown screens or CRISPR knockout libraries.

Responsibilities

Drive data science initiatives to support informed decision-making in active early-stage small molecule and oligonucleotide discovery projects.
Collaborate with laboratory scientists to build data infrastructure, develop decision-making heuristics, and implement tracking systems for early discovery oligonucleotide and DEL projects, supporting workflows from initial screening through candidate selection.
Collaborate closely with research tech and AI/ML teams to architect, develop, and optimize predictive informatics platforms that enable scalable data integration, advanced statistical analytics, and actionable insights for therapeutic discovery.