Principal Scientist, Oncology Data Science (Translational Science)

Position Summary The GSK Oncology Data Science team in R&D Translational Science is seeking a Translational AI scientist to build ML applications for a long-sought-after problem: if we alter a patient tumor's molecular state in silico , can we predict how their clinical trajectory will change? To tackle this problem, you will integrate and validate multimodal foundation models; bridge functional genomics, spatial omics, and real-world data; and apply cutting edge causal inference techniques.

We operate with high velocity at the intersection of machine learning, causal inference, functional genomics, spatial biology, and real-world clinical data; your expertise, execution, technical leadership and communication will drive our efforts to bring the right therapies to the right patients.

Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:

- Own the pipeline and develop advanced ML architectures to integrate complex multimodal datasets, including single-cell, spatial omics, histopathology, functional genomics, and real-world clinical data.

- Partner closely with wet-lab scientists, clinicians, and pathologists to validate machine learning models, including in-silico perturbations within the tumor microenvironment against ground-truth data (counterfactual validation).

- Develop approaches to extract interpretable features from models to generate testable oncological hypotheses and link insights to clinical pipeline decisions such as asset prioritization and patient subpopulation selection.

- Contribute clean, reproducible tooling to cross-team frameworks. We enforce good engineering practices in our research-utilizing code architecture planning, clean code and automated testing to build trustworthy, reusable code.

- Maintain cutting edge knowledge of advancements, share with the team and maintain our team as a thought leader through publications in high-impact venues and engaging with the broader community.

Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

- PhD (or equivalent experience) in a quantitative field (Applied ML, Computer Science, Physics, Systems/Computational Biology, or equivalent) with 1+ years of industry or productive post-doctoral academic experience.

- Experience with deeply embedded in cancer / computational biology, with a strong understanding of tumor microenvironment dynamics and high dimensional datasets.

- Experience with analytical and modelling skills, including expertise in statistical and machine learning approaches.

- Experience in one or more of the following: statistical modelling of functional genomics screening datasets (e.g., bulk CRISPR screens, Perturb-seq) or spatial omics.

- Experience in Python and deep learning frameworks (PyTorch) for data processing and machine learning model development, with a strong grasp of software engineering fundamentals (e.g., version control, modular design, CI/CD).

Preferred Qualification If you have the following characteristics, it would be a plus:

- Experience with multi-modal integration, including spatial transcriptomics/proteomics, histopathology, and single cell omics data.

- Experience working with longitudinal clinical health record trajectory data.

- Experience with causal inference and individual treatment effect modelling.

- Experience with AI agent-driven workflows and coding tools.

- Experience with generative deep learning approaches, including flow matching, diffusion and causal transformer models.

- Excellent written and oral communication skills, with a proven ability to present complex computational concepts to technical and non-technical stakeholders.

Work model: This role is hybrid. You will balance on-site collaboration with focused remote work.

Skills Applied Statistics, Data Analysis, Data Engineering, Data Science, Datasets, Drug Development, Drug Discovery Process, Drug Target Identification, Genetic Analysis, Genomic Analysis, Machine Learning (ML), Software Engineering

- If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $121,275 to $202,125. The US salary ranges take into account a number of factors including work location within the US market, the candidate's skills, experience, education level and the market rate for the role.

In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave.

If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process.Germany Salary Range / Gehaltsbandbreiten Deutschland: EUR 74,250 to EUR 123,750For positions covered by collective bargaining, employment conditions and remuneration are determined by the applicable collective agreements for the chemical industry as well as internal policies.

More detailed information on compensation will be provided during the recruitment process. Depending on the role and internal policies, the position may also be eligible for a bonus (if applicable and based on defined, non-discretionary criteria) and/or awards for exceptional performance (granted at the employer's discretion).

All benefits arising from the applicable collective bargaining agreement, such as holiday allowance and additional paid time off, will be provided in full, in accordance with that agreement and German law. Other benefits may also be offered, which may include health & wellbeing benefits, pension plan and paid parental leave & care of family member leave.