Portfolio Revenue & Debt Data Scientist
Thames Water is looking for a skilled and driven Portfolio Revenue & Debt Data Scientist to join our dynamic Credit Risk team. This is a unique opportunity to work at the forefront of credit risk analytics, helping to shape smarter collections strategies, reduce bad debt, and improve customer outcomes.
What you will be doing as the Portfolio Revenue & Debt Data Scientist
In this pivotal role, you’ll lead deep‑dive analysis into customer portfolio trends, build predictive models, and support the transition to our enterprise data lake. Your insights will directly influence operational improvements, policy decisions, and long‑term financial resilience.
You will also:
- Develop and maintain SQL‑based reporting solutions to drive actionable insights.
- Collaborate with the Credit Reporting & Insight team to ensure analytics meet business needs.
- Partner with the Digital Team to align data governance and infrastructure.
- Work with the Income Leadership Team to shape strategy and support decision‑making.
- Champion a culture of data‑driven thinking across the Income function.
Key Responsibilities
- Conduct root cause analysis of debt accumulation trends.
- Build and refine predictive models for credit risk and debt recovery.
- Provide insights to support the Bad Debt Transformation programme.
- Support the migration to a data lake environment, ensuring data integrity and accessibility.
- Create scalable, efficient SQL code and reporting frameworks.
- Embed analytics into strategic decision‑making across the business.
What you should bring to the role
To thrive in this role, you must be able to confidently answer YES to the following:
- Are you proficient in writing SQL queries to extract, join, and transform large datasets for MI/reporting and predictive modelling?
- Have you got experience in data cleansing, validation, and building predictive models?
- Are you proficient in Python for statistical analysis and able to relay insights to non‑technical stakeholders?
In addition, you will bring:
- Proven experience in credit risk analytics, debt management, or financial modelling.
- Experience working in cross‑functional teams and translating data into strategy.
- Familiarity with cloud platforms like Azure Data Lake, AWS, or Google Cloud.
- A degree (or equivalent experience) in Data Science, Mathematics, Statistics, or similar.
- A passion for continuous improvement and data‑led transformation.
Desirable Experience
- Experience migrating from traditional databases to data lake architecture.
- Background in Utilities or Financial Services.
- Exposure to SAP or DM9 environments.
- Knowledge of machine learning techniques relevant to credit risk.
Location: Hybrid – Walnut Court – SN2 8BN.
Hours: 36 hours per week, Monday to Friday.
Application Requirement
All applicants must include a covering letter describing a time when you added specific value to a project through your insight, inclusive of:
- The metrics impacted.
- The results delivered.
What’s in it for you?
- Competitive starting salary of £53,910 per annum.
- Annual leave: 26 days holiday per year, increasing to 30 with the length of service (plus bank holidays).
- Performance‑related pay plan directly linked to both company and individual performance measures and targets.
- Generous Pension Scheme through AON.
- 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.
Who are we?
We’re the UK’s largest water and wastewater company, with more than 16 million customers relying on us every day to supply water for their taps and toilets. We want to build a better future for all, helping our customers, communities, people and the planet to thrive.
We’re committed to being a great, diverse, and inclusive place to work. We welcome applications from everyone and want to ensure you feel supported throughout the recruitment process. If you need any adjustments, we’re here to help and support.
Disclaimer: due to the high volume of applications we receive, we may close the advert earlier than the advertised date, so we encourage you to apply as soon as possible to avoid disappointment.
