Machine Learning Engineer - Deep Learning
Posted 1 week ago by Harnham
£700 Per day
Inside
Undetermined
London, England, United Kingdom
Summary: The role of Contract Data Scientist focuses on developing advanced forecasting models and applying causal AI techniques for high-profile retail clients. The position requires hands-on experience in deep learning and data science, operating within a modern MLOps environment. The contract is for four months, with an immediate start, and emphasizes impactful project work in a fast-paced setting. Candidates should be adept at communicating technical concepts effectively.
Key Responsibilities:
- Designing and building custom forecasting models (XGBoost, deep learning, RL) for real-world retail scenarios
- Applying causal and graph-based methods to understand and optimise customer behaviour
- Working across the full stack of data science, from data wrangling to deployment
- Operating within a modern MLOps setup (Docker, CI/CD, AWS)
- Contributing to product-facing tooling (bonus if you've used JavaScript / Next.js / TypeScript)
Key Skills:
- Strong practical knowledge of deep learning fundamentals - ideally with PyTorch
- Experience building bespoke models for time series, tabular, image, or text data
- Hands-on forecasting experience with retail or consumer datasets
- Skilled in causal inference, graph AI, or reinforcement learning
- Comfortable deploying models in production environments
- Strong communicator, able to clearly explain technical choices and trade-offs
- Exposure to JavaScript, TypeScript, or Next.js (nice to have)
- Prior work in a startup, agency, or consultancy environment (nice to have)
Salary (Rate): £700.00/daily
City: London
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: inside IR35
Seniority Level: undetermined
Industry: IT
Contract Data Scientist - Forecasting, Deep Learning, Causal AI (Retail) £600-700/day | 4 Months | Start ASAP | Inside IR35
RAPP is looking for a technically sharp, hands-on Data Scientist to join their agile consulting team supporting high-profile retail clients. This is a 4-month contract with the chance to work on impactful forecasting and causal AI projects within a fast-moving, agency-style environment.
What You'll Be Doing:
- Designing and building custom forecasting models (XGBoost, deep learning, RL) for real-world retail scenarios
- Applying causal and graph-based methods to understand and optimise customer behaviour
- Working across the full stack of data science, from data wrangling to deployment
- Operating within a modern MLOps setup (Docker, CI/CD, AWS)
- Contributing to product-facing tooling (bonus if you've used JavaScript / Next.js / TypeScript)
What We're Looking For:
- Strong practical knowledge of deep learning fundamentals - ideally with PyTorch
- Experience building bespoke models for time series, tabular, image, or text data
- Hands-on forecasting experience with retail or consumer datasets
- Skilled in causal inference, graph AI, or reinforcement learning
- Comfortable deploying models in production environments
- Strong communicator, able to clearly explain technical choices and trade-offs
Nice to Have:
- Exposure to JavaScript, TypeScript, or Next.js
- Prior work in a startup, agency, or consultancy environment
This is a fast-paced project with immediate impact - if you're a data scientist who thrives on building models from scratch and solving tough commercial problems, this one's for you.
Desired Skills and Experience
- Python
- PyTorch
- XGBoost
- Deep Learning
- Forecasting
- Causal AI
- Graph AI
- Reinforcement Learning
- Time Series
- AWS
- Docker
- CI/CD