Data Scientist
Centrica is hiring a Data Scientist in Cardiff, United Kingdom. Level rates it ; you can apply on Centrica.
Level's read on this job
- Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include XGBoost, MLflow and Databricks.
- Level lists 57 other open roles at Centrica.
- The role was posted in the last few days.
AI in this role
Join us, be part of more.
We’re so much more than an energy company. We’re a family of brands revolutionising how we power the planet. We're energisers. One team of 21,000 colleagues that's energising a greener, fairer future by creating an energy system that doesn’t rely on fossil fuels, whilst living our powerful commitment to igniting positive change in our communities. Here, you can find more purpose, more passion, and more potential. That’s why working here is #MoreThanACareer. We do energy differently - we do it all. We make it, store it, move it, sell it, and mend it.
We have an exciting opportunity for a Data Scientist to join our team, using relevant data to tackle complex business challenges and turn insight into measurable outcomes.
You'll work across a varied portfolio of customer, commercial and operational problems, from understanding churn and retention to Customer Lifetime Value (CLTV), segmentation, propensity modelling and operational optimisation.
This is more than a model-development role. You'll have the opportunity to build reusable, production-ready solutions, embed models into business processes and see the real-world impact of your work.
Key Accountabilities:
- Design, build, test and deploy statistical and machine learning models to solve customer, commercial and operational challenges.
- Analyse large and complex datasets to identify patterns, opportunities and drivers of customer behaviour, translating your findings into clear, actionable recommendations.
- Partner with stakeholders to turn commercial questions and opportunities into well-defined data science problems.
- Develop solutions across areas including churn and retention, CLTV, segmentation, propensity modelling, customer targeting and optimisation.
- Build robust feature engineering, training and inference pipelines, using technologies such as Python, PySpark and Databricks.
- Help take models into production through automated workflows, testing, version control, CI/CD, monitoring and model lifecycle management.
- Collaborate with data engineers, platform teams and fellow data scientists to create scalable, reliable solutions while maintaining high standards of data quality, model governance and responsible AI.
Here’s what we’re looking for:
- Experience applying statistical analysis and machine learning to real-world business problems.
- Strong programming skills in Python and SQL, with experience working with large and complex datasets.
- Experience with, or exposure to, PySpark and Databricks.
- A good understanding of machine learning techniques, which could include classification, regression, gradient boosting/XGBoost, segmentation, propensity modelling or survival analysis.
- Experience or knowledge of production ML practices such as Git, CI/CD, MLflow, automated testing, model deployment and monitoring.
- Strong analytical thinking, with the ability to take an ambiguous business question through exploration and modelling to an actionable recommendation.
- The ability to explain complex analytical approaches, assumptions and results clearly to both technical and non-technical audiences.
- A collaborative and curious approach, with a willingness to learn, challenge existing thinking and share knowledge.
Experience within customer analytics, energy, utilities, retention, pricing or commercial analytics would be beneficial, but isn't essential.
Location:
We operate a Flexible First approach, giving colleagues flexibility in how and where they work. This role can be predominantly home-based, although ideally you will be based within commutable distance of our Cardiff site and be flexible to travel across our sites as may be needed on occasion for team meetings.
Why should you apply?
We’re not a perfect place – but we’re a people place. Our priority is supporting all of the different realities our people face. Life is about so much more than work. We get it. That’s why we’ve designed our total rewards to give you the flexibility to choose what you need, when you need it, making sure that you and your family are supported not only financially, but physically and emotionally too. Visit the link below to discover why we’re a great place to work and what being part of more means for you.
https://www.morethanacareer.energy/centrica
If you're full of energy, fired up about sustainability, and ready to craft not only a better tomorrow, but a better you, then come and find your purpose in a team where your voice matters, your growth is non-negotiable, and your ambitions are our priority.
Help us, help you. We would love for you to share any information about yourself throughout our recruitment process so that we can better understand you and help shape your journey.
How we rate this
Data Scientist at Centrica rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you think about the risk of an AI system in this kind of role failing silently?
- Walk me through how you've used XGBoost in your day-to-day work.
- What are the limits of MLflow that you've run into, and how did you work around them?
- What's a project where you used Databricks hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: AI Safety, XGBoost, MLflow, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
Get your CV reviewed before you apply
Send your CV and the role you want next. Your review arrives by email within 3 days.
Get new data scientist jobs (Builds AI ●●●●) by email
One email a week, each job rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free.
Similar roles
Data roles that build AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step