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# Applied Scientist, ML

- **Company:** Roc Search
- **Location:** London, United Kingdom
- **Salary:** £90,000 - £110,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Computer Simulation, Distributed Systems, Dynamical Systems, Experimental Data, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Reinforcement Learning, Pytorch, Uncertainty Quantification (UQ), Scikit Learn, Information Technology, Free and Open-Source Software, Machine Learning Operations
- **Published:** November 5, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/applied-scientist-ml/54973291

## About the Role

Degree in Mathematics, Statistics, Physics, Engineering, Computer Science or another relevant quantitative discipline.
Strong experience in machine learning, statistical modelling or applied mathematical research.
Practical experience with causal inference, experimental design, hypothesis testing or statistical analysis.
Strong Python programming skills and familiarity with ML frameworks such as PyTorch, JAX or scikit-learn.
Experience working with complex datasets, time-series data or dynamic systems.
Strong analytical and problem-solving skills, with the ability to translate theoretical concepts into practical solutions.
Comfortable working across research, experimentation and applied engineering.

Desirable Experience
PhD or MSc in Statistics, Applied Mathematics, Machine Learning, Engineering, Physics or a related discipline.
Causal discovery, causal modelling, Bayesian inference or probabilistic machine learning.
Reinforcement learning, decision science or sequential optimisation.
Simulation, digital twins or physics-based modelling.
Control theory, optimal control or model predictive control.
Experience with uncertainty quantification, sensitivity analysis or system identification.
Distributed computing, federated learning or deployment of ML models.
Publications, open-source contributions or experience in applied scientific research.

## Description

We are seeking an Applied Scientist specialising in Machine Learning, Causal Inference and Mathematical Modelling to develop intelligent solutions for complex, dynamic systems.
You will work across the full ML lifecycle, from research and problem formulation through to experimentation, model development and deployment. Working closely with engineers and domain specialists, you will use statistical methods, causal reasoning and machine learning to understand complex systems, identify underlying relationships and develop solutions that can be applied in real-world environments.

The role offers a balance of scientific research and practical engineering, with the opportunity to influence technical direction and contribute to innovative AI applications., Develop and apply machine learning, statistical and causal inference methods to complex real-world problems.
Design experiments and develop methodologies to identify causal relationships, test hypotheses and understand system behaviour.
Translate complex and ambiguous problems into structured mathematical and computational models.
Analyse time-series, observational and experimental data to identify patterns, dependencies and underlying mechanisms.
Develop, evaluate and refine predictive and decision-making models using modern ML techniques.
Apply simulation, optimisation and mathematical modelling to investigate complex or dynamic systems.
Collaborate with software engineers and research teams to implement and deploy models into practical applications.
Contribute to research, experimentation and the development of novel methodologies.