Applied Scientist, ML

Applied Scientist - Machine Learning & Causal Inference

London | Hybrid | £90,000-£110,000 (DOE) + Bonus + Equity

About the Company

An ambitious, well-funded technology scale-up developing advanced AI solutions to tackle complex real-world challenges. Following recent investment, the business is expanding its technical team to accelerate the development of innovative machine learning technologies.

This is an opportunity to join a growing, highly technical team working at the intersection of AI research, applied mathematics, engineering and real-world applications.

The Role

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.

Key Responsibilities

• 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.

Essential Experience

• 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.

Why Join?

• Work on innovative AI with meaningful real-world impact.
• Join a well-funded, rapidly growing technology business.
• Collaborate with talented scientists and engineers across multiple disciplines.
• Enjoy significant technical ownership and influence over research direction.
• Bridge cutting-edge research with practical, real-world applications.