Principal Data Scientist, Analytics & Machine Learning

Summary

• Responsibility : Plan and execute Valink’s data strategy to build predictive models for drug discovery and drug positioning
• Salary : Competitive salary at Principal/Associate Director level
• 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 to 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