> Markdown version of [/jobs/ext/3231925-data-scientist](https://www.wearedevelopers.com/jobs/ext/3231925-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** Unity Group
- **Location:** Chorley, UK (Remote available)
- **Experience:** Experienced
- **Salary:** £58,000.0 - £62,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, A/B Testing, Application Programming Interfaces (APIs), Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Databases, Database Queries, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Computational Statistics, Data Processing, Cloud Platform System, Sql Optimization, Pytorch, Scikit Learn, Information Technology, Xgboost, Enterprise Integration, Machine Learning Operations, Software Library
- **Published:** September 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=06cd6805f823ce61

## About the Role

* 3+ years of experience as a Data Scientist, Machine Learning Engineer, or Quantitative Researcher.
 * Strong technical background in Python or R for data manipulation and statistical computing.
 * Practical experience with machine learning libraries (scikit-learn, XGBoost, TensorFlow, PyTorch) and Advanced SQL for database querying.
 * Solid foundation in linear algebra, multivariable calculus, probability theory, and statistical hypothesis testing.
 * Must have the right to work in the UK without requiring employer sponsorship.
 * Desirable: Master's or PhD degree in Data Science, Computer Science, Economics, Mathematics, or Physics; experience with cloud environments (AWS/Azure).

## Description

* Predictive Modeling & ML Development: Design, build, and deploy supervised and unsupervised machine learning algorithms (regression, classification, clustering, time-series forecasting).
 * Data Processing & Pipeline Integration: Clean, feature-engineer, and structure large-scale datasets from disparate databases to prepare training data for algorithmic models.
 * Statistical Analysis & Hypothesis Testing: Conduct robust statistical modeling, econometric evaluations, and experimental A/B testing to evaluate product features and operational changes.
 * Model Deployment & Monitoring: Partner with MLOps and software engineering teams to transition models from prototype stage into production APIs, monitoring performance drift over time.
 * Commercial Insights & Storytelling: Present complex analytical findings, probabilistic forecasts, and technical insights to executive stakeholders.