At a Glance
- Tasks: Lead data science to drive commercial value and customer insights in an AI-driven energy tech scale-up.
- Company: Exciting AI-driven energy tech scale-up focused on reducing energy waste.
- Benefits: Competitive salary, hybrid work, 28 days holiday, and training budget.
- Other info: Opportunity to lead a growing team and influence core business functions.
- Why this job: Shape the future of energy efficiency and make a real-world climate impact.
- Qualifications: Strong applied ML background and excellent Python skills required.
The predicted salary is between 85000 - 95000 £ per year.
Location: London (one day a week onsite)
Salary: up to £95,000
I'm working with an exciting AI-driven energy tech scale-up that's using advanced analytics, machine learning and real-world electrical data to help commercial buildings dramatically reduce energy waste and improve operational performance. They're now looking for a Head of Data Science & Commercial Insights to join their leadership team and take ownership of how data science translates into real product value, customer impact and commercial outcomes.
This is a high-impact role sitting at the intersection of data science, product and commercial strategy - ideal for someone who wants to move beyond pure model-building and shape how analytics directly drives growth, sales and customer success.
The Mission
The business is tackling one of the biggest inefficiencies in the built environment: wasted energy in heating, cooling and asset operation. Their platform turns high-frequency IoT and electrical data into actionable insight — helping customers identify inefficiencies, reduce consumption, and move toward predictive maintenance and ESG goals. They've recently secured further funding and are now scaling their data science capability into a core commercial function.
The Role
This is not a research-only leadership position. They need someone who can own the bridge between data science and commercial value, ensuring everything they build is useful, usable and used.
You will:
- Translate data science outputs into customer-facing insight, product features and commercial evidence
- Shape the roadmap around real customer problems and business priorities
- Work closely with CTO, COO, Product, Engineering and Commercial teams
- Turn complex energy, HVAC and asset data into clear, trusted insight that drives decisions
- Lead delivery from prototype → production → scalable product capability
- Ensure models are robust, deployed, monitored and maintainable
- Lead and mentor a growing data science team (initially including 1 direct report)
- Support customer pilots, demos, investor updates and proof-of-value work
- Help define what 'good' looks like for analytics, dashboards and insight outputs
Technical & Delivery Focus
You'll still be hands-on and expected to lead by example technically, including:
- Applied ML on time-series / IoT / sensor / energy / building data
- Python (NumPy, pandas, scikit-learn, etc.)
- Productionisation of models into scalable workflows
- MLOps practices (e.g. MLflow, Airflow / Prefect, Docker)
- Building reproducible, well-tested data science pipelines
- Working with noisy, real-world operational datasets
- Strong model evaluation, monitoring and deployment practices
What Success Looks Like
- Data science outputs consistently becoming live product features
- Clear commercial impact from analytics (sales, renewals, customer value)
- Reduced 'ad hoc analysis' in favour of repeatable, scalable pipelines
- Strong customer-facing insight used in demos, pilots and reporting
- A high-performing DS function that is both technically credible and commercially sharp
What They're Looking For
- Strong applied ML background (ideally time-series / IoT / energy / industrial data)
- Proven ability to turn data science into product or commercial value
- Excellent Python skills and production-level engineering discipline
- Experience building and deploying ML pipelines in real environments
- Comfortable working with messy, high-volume real-world data
- Strong communicator - able to translate complex outputs clearly
- Experience in small, fast-moving teams or scale-ups
- Leadership experience (or clear readiness to step into it)
The Package
- £85,000 – £95,000 depending on experience
- Hybrid (London SE1) – minimum 1 day per week in office
- 28 days holiday + bank holidays
- Pension scheme
- Flexible working
- Training & development budget
- Equipment provided
- Opportunity to shape a core function in a scaling business
Why This Role?
This is a chance to step into a role where data science is not a support function — it is the product. You'll be shaping how a growing AI platform turns raw operational data into measurable energy savings, stronger customer outcomes and real-world climate impact.
For more information, please contact Hannah from Oakmont Consulting.
Head of Data Science (Commerical Insights) in London employer: Oakmont Consulting
Join an innovative AI-driven energy tech scale-up in London, where you'll play a pivotal role in transforming data science into tangible commercial value. With a strong focus on employee growth, flexible working arrangements, and a collaborative culture, this company empowers you to lead a high-performing data science team while making a significant impact on energy efficiency and sustainability. Enjoy competitive benefits, including a generous holiday allowance and a dedicated training budget, as you help shape the future of energy management.
StudySmarter Expert Advice🤫
We think this is how you could land Head of Data Science (Commerical Insights) in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your best projects, especially those related to data science and commercial insights. This will give you an edge when discussing how you can add value to the team.
✨Tip Number 3
Prepare for interviews by understanding the company’s mission and how they use data science. Be ready to discuss how your experience aligns with their goals, especially in reducing energy waste and improving operational performance.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team.
We think you need these skills to ace Head of Data Science (Commerical Insights) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Head of Data Science. Highlight your experience with applied ML, especially in time-series and IoT data, and showcase how you've turned data insights into commercial value.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about reducing energy waste and how your skills align with our mission. Be specific about your leadership experience and how you can bridge the gap between data science and commercial outcomes.
Showcase Your Technical Skills:Don’t shy away from detailing your technical prowess! Mention your experience with Python, MLOps practices, and building scalable data pipelines. We want to see how hands-on you are with real-world datasets.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Oakmont Consulting
✨Know Your Data Science Inside Out
Make sure you’re well-versed in applied machine learning, especially in the context of time-series and IoT data. Brush up on your Python skills and be ready to discuss how you've turned complex data into actionable insights in previous roles.
✨Connect Data Science to Commercial Value
Prepare examples of how your data science work has directly impacted commercial outcomes. Think about specific projects where your insights led to increased sales or improved customer satisfaction, and be ready to share these stories.
✨Showcase Your Leadership Skills
Even if you haven’t held a formal leadership position, think about times when you’ve mentored others or led a project. Be prepared to discuss your approach to leading a team and how you would support and develop your data science team in this role.
✨Communicate Clearly and Confidently
Practice explaining complex data concepts in simple terms. You’ll need to demonstrate that you can translate technical outputs into clear, customer-facing insights, so consider rehearsing with someone who isn’t in the field to ensure your explanations are accessible.
