What you’ll be doing
as a Data Scientist
1. Design and develop machine learning models to support use cases such as predictive maintenance, leakage detection, and customer behaviour analytics.
2. Extract, clean, and transform large datasets from multiple sources to improve model accuracy and deliver insights.
3. Apply statistical and predictive modelling techniques to inform business decisions and optimise operations.
4. Use data visualisation tools to communicate insights clearly to non-technical stakeholders.
5. Work closely with Data Engineers, BI Developers, and Product Teams to ensure seamless integration of models into business processes.
6. Implement MLOps best practices to ensure models are scalable, robust, and well-maintained in production environments.
7. Collaborate with IT, security, and governance teams to ensure data ethics, privacy, and compliance with GDPR and Ofwat regulations.
8. Contribute to open data initiatives and knowledge-sharing activities across the team and wider organisation.
9. Stay current with industry trends and emerging technologies in AI/ML and data science.
10. Support stakeholder engagement across operational, engineering, retail, and finance functions to identify opportunities for AI adoption.
Base Location: Reading – Hybrid
Working Pattern: 36 Hours
What you should bring to the role
Essential Experience:
11. Proven experience developing and deploying machine learning models in a commercial or operational setting.
12. Strong background in data science and statistical modelling, with experience in predictive analytics and advanced analytics techniques.
13. Experience handling large, complex datasets in cloud or distributed environments.
14. Ability to translate business problems into data science solutions with measurable outcomes.
15. Collaborative experience working with engineers, analysts, and business stakeholders on end-to-end data projects.
16. Experience in data science, machine learning, or analytics, including leadership of high-impact projects or strategic model deployments.
Essential Technical Skills & Qualifications
17. Proficiency in Python, R, or a similar programming language for data analysis and machine learning.
18. Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
19. Familiarity with cloud-based AI/ML platforms (e.g. Azure ML, AWS SageMaker, Databricks).
20. Working knowledge of SQL and data engineering principles including pipelines and ETL processes.
21. Experience using data visualisation tools such as Power BI or SAP Analytics Cloud.
Desirable Experience
22. Experience within the water or utilities industry.
23. Exposure to IoT or sensor-based data analytics.
24. Experience applying AI to asset management or predictive maintenance challenges.
25. Working knowledge of natural language processing (NLP) techniques or chatbot applications.
26. Experience deploying and monitoring ML models in production using MLOps frameworks.
Desirable Technical Skills & Qualifications
27. Master’s or PhD in Data Science, Computer Science, Statistics, or a related field.
28. Cloud certifications (e.g. Azure AI Engineer, AWS Machine Learning Speciality).
29. Knowledge of advanced data science techniques such as deep learning, reinforcement learning, or Bayesian methods
30. Proficiency in advanced programming techniques for scaling and optimising ML models.
What’s in it for you?
31. Competitive salary up to £100,000 per annum, depending on experience.
32. Annual Leave - 26 days holiday per year, increasing to 30 with the length of service. (plus bank holidays)
33. Generous Pension Scheme through AON.
34. Performance-related pay plan directly linked to company performance measures and targets
35. Access to lots of benefits to help you take care of you and your family’s health and wellbeing, and your finances – from annual health MOTs and access to physiotherapy and counselling, to Cycle to Work schemes, shopping vouchers and life assurance.