Hybrid Data Scientist - ML & AI Modeling for Impact

Marshall Wolfe; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Marshall Wolfe is seeking a Data Scientist in London, offering a hybrid role with three days in the office. You will join an engineering team building advanced machine learning solutions that help organisations make smarter, data-driven decisions.

The ideal candidate has a strong academic background in CS, statistics or math, with hands-on Python/SQL skills and experience with PyTorch or TensorFlow. You’ll own end-to-end modeling, experiments and deployment in production.

Qualifications

• Degree in CS, Stats, Math, ML or related quantitative discipline.
• Hands-on experience using AI coding agents (Codex/Claude/Cursor) to write and test code.
• Strong Python and SQL skills.
• 2–5 years commercial experience in Data Science/Analytics/ML.
• Solid understanding of ML and statistical modelling.
• Experience with PyTorch or TensorFlow is advantageous.
• Self-motivated, curious and eager to learn.

Responsibilities

• Build, train and optimise machine learning models.
• Analyse large datasets to identify trends and insights.
• Develop predictive models for customer behaviour, churn and lifetime value.
• Apply statistical analysis and experimentation techniques.
• Collaborate with engineers to deploy and monitor models in production.

Skills

Python

SQL

Data analysis

Experimentation

Education

Bachelor's degree in Computer Science, Statistics, Mathematics, Machine Learning or related field

Tools

Codex

Claude

Cursor

PyTorch

TensorFlow

Job description

Marshall Wolfe is seeking a Data Scientist in London, offering a hybrid role with three days in the office. You will join an engineering team building advanced machine learning solutions that help organisations make smarter, data-driven decisions.

The ideal candidate has a strong academic background in CS, statistics or math, with hands-on Python/SQL skills and experience with PyTorch or TensorFlow. You’ll own end-to-end modeling, experiments and deployment in production.