Head / Principal Data Scientist, Analytics & Machine Learning

Join to apply for the Head / Principal Data Scientist, Analytics & Machine Learning role at Valink Therapeutics

Salary: Competitive salary at Director or Principal level, depending on experience.

Location: White City, London, UK

Starting date: Jan-Feb 2026

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: in addition to shaping Valink’s strategic input on data analysis approaches and AI‑driven predictive modelling, the profile will build prototype pipelines, curate relevant datasets, and validate methodologies that will inform the long‑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.
• 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 for biologists.
• 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 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 (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 disciplines.
• 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

Seniority level

Mid‑Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

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