Applied ML Scientist: Causal Inference & Modelling (Hybrid)

Roc Search Inc.; Greater London; Hybrid; GBP 90,000 - 110,000; Full time

Job summary

Roc Search Inc. is seeking an Applied Scientist specialising in Machine Learning, Causal Inference and Mathematical Modelling to develop intelligent solutions for complex, dynamic systems in London.

The role spans research and practical engineering, with responsibilities across problem formulation, experimentation and deployment. You will work with engineers and domain experts to apply statistical methods, causal reasoning and ML to real-world applications, influencing technical directions and

Qualifications

• Degree in Mathematics, Statistics, Physics, Engineering, Computer Science or a related quantitative field.
• Strong experience in ML, statistical modelling or applied mathematics.
• Hands-on experience with causal inference and experimental design.

Responsibilities

• Develop and apply ML, statistical and causal inference methods to real-world problems.
• Design experiments to identify causal relationships and test hypotheses.
• Translate problems into structured mathematical and computational models.
• Analyse time-series, observational and experimental data to identify patterns.
• Develop, evaluate and refine predictive and decision-making models.
• Apply simulation, optimisation and modelling to dynamic systems.
• Collaborate with software engineers to deploy models.
• Contribute to research and development of new methodologies.

Skills

Machine Learning

Causal Inference

Mathematical Modelling

Python

PyTorch

JAX

Scikit-learn

Time-series

Education

Degree in Mathematics/Statistics/Physics/Engineering/CS

Tools

PyTorch

JAX

Scikit-learn

Job description

Roc Search Inc. is seeking an Applied Scientist specialising in Machine Learning, Causal Inference and Mathematical Modelling to develop intelligent solutions for complex, dynamic systems in London.

The role spans research and practical engineering, with responsibilities across problem formulation, experimentation and deployment. You will work with engineers and domain experts to apply statistical methods, causal reasoning and ML to real-world applications, influencing technical directions and