Summary
Responsibility : Plan and execute Valink’s data strategy to build predictive models for drug discovery and drug positioning
Salary : Competitive salary at Director or Principal level, depending on experience
Starting date : Jan-Feb 2026
Location : White City, London, UK
About Us
Valink Therapeutics is a spinout from the University of Oxford with a mission to revolutionise the field of bispecific antibody‑drug conjugate (bsADC) discovery. We have developed the most advanced drug discovery platform for complex modalities, capable of generating and screening over 1,000 drug candidates per week to uncover novel, first‑in‑class bsADCs that others cannot. Our approach generates a rich dataset capturing cytotoxicity, target expression, payload sensitivity, and related correlations, offering high potential for integration into predictive models that accelerate and enhance future drug discovery efforts.
Role Summary
The Principal Data Scientist will help shape and execute our strategy for integrating proprietary screening data with internal & external biological datasets to identify and prioritise drug candidates for testing. This role is hands‑on: on top of shaping Valink’s strategic input on data analysis approaches and AI‑driven predictive modelling, the profile will also build prototype pipelines, curate relevant datasets, and validate methodologies that will inform the longer‑term development of our in‑house AI platform.
Key Responsibilities
Driving Data Strategy
Determine appropriate models and computational frameworks for predictive drug‑target sensitivity analysis.
Inform on infrastructure, data architecture, and workflow considerations for scalable AI adoption.
Data Curation, Integration and Analytics
Identify, source, and curate publicly available datasets (cell lines, patient data, target expression, protein/compound databases).
Harmonise and integrate these external resources with Valink’s internal phenotypic and screening data.
Ensure data quality, interoperability, and relevance for downstream predictive modelling.
Perform EDA to uncover patterns, trends and outliers in our data to augment downstream design and modelling processes.
Model Prototyping & Development
Build and test machine learning pipelines to predict correlations between cytotoxicity, target expression, and payload sensitivity.
Explore applications of AI‑driven drug positioning approaches to support candidate selection.
Benchmark different models and methods and evaluate trade‑offs to derive the best model.
Turn model outputs into clear insights and visualisations that biologists can act on, helping teams move from hit discovery to candidate optimisation.
Collaboration & Knowledge Transfer
Act as a technical partner to the Platform and Asset teams, translating research questions into AI solutions.
Work alongside the wet lab scientists to design new screening campaigns, using model predictions to guide assay set‑ups and hit selection.
Provide clear documentation, recommendations, and interim solutions that can be scaled internally.
Essential Requirements
PhD or MSc in Computational Biology, Bioinformatics, Computer Science, or related discipline.
5+ years of experience in industry or academia applying biostatistics and machine learning to biomedical datasets, particularly in areas such as drug positioning, drug repurposing, pharmacogenomics, or precision medicine.
Demonstrated ability to work with large‑scale public datasets (e.g., DepMap, CCLE, LINCS, GDSC, TCGA, UniProt).
Expertise in building data pipelines and predictive models using Python/R and ML frameworks (e.g., scikit‑learn, TensorFlow, PyTorch).
Solid grasp of relational databases and proficiency in writing SQL queries
Familiarity with high‑throughput screening data, cytotoxicity assays, or drug sensitivity profiling a strong plus.
Hands‑on, problem‑solving mindset with the ability to balance strategic advisory with technical execution.
Strong communication skills and ability to collaborate across discipline
Proficient in mathematical and statistical skills required for machine learning and AI
Desirable
Experience leading complex scientific projects in an industrial research setting working alongside wet lab scientists.
Familiarity with laboratory automation, high‑throughput screening, and experimental design for drug discovery.
Familiarity with cloud computing environments for large scale data analysis.
What we offer
Competitive salary
Stock option plan
25 days of holiday, plus bank holidays
Bupa private medical insurance and life assurance
YuLife wellbeing engagement
Matched pension
Flexible working hours
Hybrid working location
Cyclescheme
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