Artificial Intelligence Consultant

AI Consultant - Biomedical Data & Machine Learning

We are supporting a start-up biotech company pioneering the development of bispecific antibody-drug conjugates (ADCs) and at the forefront of integrating AI-driven insights into drug discovery. Our cutting-edge platform leverages high-throughput screening to generate large-scale biological datasets, enabling precision medicine and accelerating the discovery of novel drug candidates. We are looking for an AI Consultant to join our team, specializing in machine learning pipelines, biomedical data integration, and predictive model development. The ideal candidate will have a strong background in AI, a passion for biomedical research, and the ability to develop scalable AI solutions to support the discovery of drug candidates.

Key Responsibilities:

• Advising & Strategy: Guide the application of machine learning to high-throughput screening data and support the identification of computational frameworks to predict drug-target sensitivity.
• Data Curation & Integration: Curate and integrate publicly available and proprietary datasets (e.g., CCLE, DepMap, TCGA) with internal screening data, ensuring quality and relevance for predictive modelling.
• Model Development & Prototyping: Design, develop, and deploy machine learning pipelines to uncover insights into cytotoxicity, payload sensitivity, and target expression.
• Knowledge Transfer & Collaboration: Act as a key technical advisor to cross-functional teams, translating biological research questions into AI-driven solutions and collaborating closely with screening, biology, and computational teams.

Desired Qualifications:

• PhD or MSc in computational biology, bioinformatics, computer science, or related field.
• Proven experience applying machine learning to biomedical datasets, particularly in drug repurposing, pharmacogenomics, or precision medicine.
• Strong communication skills with the ability to translate complex technical concepts into actionable insights for non-technical stakeholders.
• Experience developing scalable machine learning pipelines using Python/R and frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Familiarity with version control systems (e.g., Git) and reproducible workflow practices.
• Experience with large-scale public biomedical datasets (e.g., CCLE, DepMap, TCGA, GDSC, UniProt) and a demonstrated track record of leveraging these datasets for drug discovery.