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# Quantitative Data Scientist

- **Company:** THE OPPORTUNITY HUB UK LTD
- **Location:** Nottingham, UK
- **Salary:** £43,500.0 - £56,500.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Big Data, Machine Learning, Tensorflow, SQL Databases, Pytorch
- **Published:** October 23, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6adb0b49fc1374228e980a4e/apply

## About the Role

Solid grounding in statistics, probability and applied machine learning, not just theory

Strong Python (pandas, scikit-learn, and ideally some exposure to PyTorch or TensorFlow)

Comfortable writing production-grade SQL against large, imperfect datasets

A few years' experience as a Quantitative Data Scientist or similar, with evidence of models that made it past a notebook

Able to explain a technical finding to someone who doesn't want the maths, just the answer

Curious by default. The best people in this role ask "does this actually hold up" before they ship anything

## Description

Perfect role for a mid professional looking to grow their AI & Technology career in Nottingham.

AI-driven products live or die on the quality of the data behind them. This growing technology company, an established SME with a reputation for solid engineering, needs a Quantitative Data Scientist who can turn messy, high-volume data into models that actually ship. It's a chance to work close to the product, not buried three layers away from it.

Here's what you'll be doing:

Build and validate statistical and machine learning models that feed directly into live AI & Technology products

Clean, structure and interrogate large datasets to find patterns the business can act on

Design experiments (A/B tests, causal inference studies) and report results in plain English to non-technical stakeholders

Work with engineers to get models into production, then monitor how they perform once they're live

Write clear documentation so your work can be picked up, audited and improved by others

Keep an eye on data quality upstream, flagging issues before they become someone else's problem