Product Data Science Lead

Octopus Energy Group
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Science Manager – Property Tech – London

Data Science Lead

Data Science Lead

Data Science Lead

Data Science Manager

Head of Data Science

Octopus is a true leader in advancing the global energy sector, delivering impactful products such as Zero Bills homes and intelligent time of use tariffs, all whilst building worldwide renown for its customer service. In fact, we are the only one of the big six energy providers in the UK that has a positive net promoter score, and we are starting to replicate that success across the world.

We're looking for an experienced data scientist to build products that will accelerate the green transition and delight our customers. This is a fantastic opportunity to work on core data problems at a company passionate about building great technology to change how customers use energy and move us closer to Net Zero. The successful candidate will report directly to the Global Head of Product, a member of Octopus’ senior leadership team.

What You’ll Do
  • Lead a new team, leveraging cutting-edge data science to develop products that accelerate the energy transition and enhance customer experience.
  • Collaborate with product leadership and engineering teams to define the product roadmap.
  • Analyze product usage to monitor performance and inform prioritisation.
  • Mentor junior data scientists and contribute to the growth of the data science community.
  • Develop and implement A/B tests and other experimental designs to measure product impact.
  • Communicate complex data findings and recommendations clearly and concisely.
  • Stay up-to-date with industry trends and advancements in data science and analytics.
What You’ll Have
  • End-to-end experience in designing and building data products using large datasets.
  • A passion for both hands-on data work and leading teams to produce high-quality output.
  • Experience managing and nurturing junior data scientists.
  • Hands-on experience with cutting-edge tools, including those in our data platform stack.
  • Experience in utility companies or other data-intensive industries.
  • Broad experience applying various analytical techniques at companies of different sizes.
Our Data Platform Stack
  • We employ software engineering best practices to design, test, and deploy our data platform and services using the following technologies: Python, Databricks, Kubernetes, Terraform, Streamlit, Airflow, Circle CI, Parquet, Delta, Spark, DBT, and SQL.
Why Else You'll Love It Here
  • We offer a unique culture where people learn, decide, and build quicker, with autonomy and amazing co-owners. We want your hard work to be rewarded with perks you actually care about.
  • Octopus Energy Group is a best company to work for, with a top-rated culture and senior leadership. We empower our people and offer a wide range of benefits.
  • We are an equal opportunity employer, committed to providing equal opportunities, an inclusive work environment, and fairness for everyone.

If this sounds like you, we'd love to hear from you. We encourage you to apply, even if you don't meet 100% of the job requirements. We're looking for genuinely decent people who are honest and empathetic.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.