Senior Machine Learning Scientist (Generative Modelling)

Relation
London
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: phdSkills: ["Communication","Collaboration","Ownership","Problem-solving","Interdisciplinary teamwork"]

Build generative and predictive ML models for understanding and controlling cellular decision-making using single-cell multi-omics, perturbation experiments, and interventional datasets. Design models that learn intervention effects, counterfactual predictions, and generalisation, then collaborate with experimental teams to iteratively select and validate the highest-signal next experiments. Evaluate models for causal coherence and mechanistic fidelity, and communicate results across interdisciplinary stakeholders.

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FursaFursa
Relation
Relation
3 months ago

Senior Machine Learning Scientist (Generative Modelling)

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Source: Company careers pageValidated by: Fursa AI
Last checked: 9 hours agoStatus: Live

Job Summary

Build generative and predictive ML models for understanding and controlling cellular decision-making using single-cell multi-omics, perturbation experiments, and interventional datasets. Design models that learn intervention effects, counterfactual predictions, and generalisation, then collaborate with experimental teams to iteratively select and validate the highest-signal next experiments. Evaluate models for causal coherence and mechanistic fidelity, and communicate results across interdisciplinary stakeholders.
Location: London
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design and implement generative modelling approaches to learn intervention effects from diverse biological data, including single-cell perturbation experiments.
  • •Develop models that move beyond correlation toward generalisation, counterfactual prediction, and experimental design.
  • •Collaborate with experimental teams to design and validate computational hypotheses through iterative strategies that identify the highest-signal next experiment.
  • •Evaluate models for causal coherence, mechanistic fidelity, and utility in guiding real-world interventions.
  • •Communicate findings clearly to colleagues and stakeholders across disciplines.

Key Requirements

  • •PhD in ML, statistics, computer science, or a related quantitative field.
  • •Deep expertise in generative modelling.
  • •Strong foundations in probabilistic modelling and representation learning (or neural network architectures) for structured or sequential data.
  • •Excellence in Python and familiarity with scalable ML tooling and high-performance computing.
  • •Experience designing and running model evaluation experiments that test real-world utility beyond standard benchmarks.
Experience:Biological dataSingle-cellMulti-omicsInterdisciplinary
Education:PhD / Doctorate
Skills:CommunicationCollaborationOwnershipProblem-solvingInterdisciplinary teamwork
Tech Stack:PythonGenerative modellingProbabilistic modellingRepresentation learningNeural network architecturesHigh-performance computingSingle-cellPerturbation experimentsMulti-omicsCounterfactual predictionCausal coherenceExperimental design

Company Brief

Relation
Relation (Relation Therapeutics) is a UK-based, technology-enabled biopharmaceutical company that combines single-cell multi-omics, patient-derived tissue, functional assays and machine learning to discover and develop novel therapeutics across immunology, metabolic and bone diseases.
Industry: Biotech
Company Size: Medium (51 to 250 employees)
Growth: Early Stage Startup
Valuation: USD 100M to 250M
Funding: Seed
Headquarters: London, United Kingdom
Founded: 2019
Glassdoor
Glassdoor: 3.8
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