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- Tasks: Lead data-driven projects in energy and renewables, using advanced analytics and AI.
- Company: High-impact organisation focused on energy transition and sustainability.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Make a real difference in the energy sector while tackling complex challenges.
- Qualifications: Experience in data science, Python, SQL, and strong stakeholder engagement skills.
- Other info: Join a collaborative culture with varied projects and genuine career advancement.
The predicted salary is between 36000 - 60000 £ per year.
A high impact organisation is expanding its Data Science and AI capability across Energy and Renewables. You’ll work close to the energy transition, helping operators, networks, investors, and innovators turn strategy into delivery, using advanced analytics, optimisation, and applied AI that performs in real world conditions.
If you enjoy complex systems, imperfect data, and decisions with real consequences, you’ll enjoy this role.
Location: UK-based, hybrid. Client site and office days vary by project needs.
The work: You’ll tackle challenges such as:
- Flexibility, storage, demand response, pricing, decision grade modelling under uncertainty
- Digital twins, sensors, operational analytics, resilience and performance improvement
- Forecasting, time series, anomaly detection, predictive maintenance
- Geospatial and environmental analytics, hazards, climate informed planning
- Pragmatic NLP and GenAI
This is delivery led, focused on solutions that land, not research for its own sake.
What you will do:
- Lead discovery with stakeholders, define success, shape the approach, and own delivery
- Build, validate, and iterate models, partnering with engineering to deploy, monitor, and improve
- Turn analysis into clear options, 'trade offs', and recommendations that drive action
- Coach others, raise standards, and contribute reusable accelerators and propositions
- Support scoping and proposals, depending on seniority
What you will bring:
- Strong hands on data science and applied AI, with delivery beyond prototypes
- Python, solid SQL, comfortable in modern data environments
- Depth in one or more of: optimisation or OR, forecasting and time series, digital twins or IoT, geospatial or climate analytics, NLP or RAG or practical GenAI
- Strong stakeholder skills, you can simplify complexity without stripping out the nuance
Why join: High-impact energy transition work, varied projects, strong craft culture, and genuine room to grow.
How to apply: Apply via the advert, or send a CV or LinkedIn profile plus a short note on the problems you like solving.
Decision Intelligence Specialist in Leeds employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Decision Intelligence Specialist in Leeds
✨Tip Number 1
Network like a pro! Reach out to people in the energy and renewables sector on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your data science projects, especially those related to decision intelligence. Use platforms like GitHub to share your code and demonstrate your problem-solving abilities. We want to see how you tackle real-world challenges!
✨Tip Number 3
Prepare for interviews by understanding the company’s projects and challenges. Research their work in energy transition and think about how your skills can contribute. We love candidates who can connect the dots between their experience and our mission!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, include a short note about the specific problems you enjoy solving – we’re keen to hear how you can make an impact in our team!
We think you need these skills to ace Decision Intelligence Specialist in Leeds
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Decision Intelligence Specialist. Highlight your experience with data science, applied AI, and any relevant projects that showcase your skills in optimisation, forecasting, or geospatial analytics.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you’re passionate about the energy transition and how your background aligns with our mission. Share specific examples of challenges you've tackled that relate to the job description.
Showcase Your Problem-Solving Skills: In your application, mention the types of problems you enjoy solving, especially those related to complex systems and imperfect data. We want to see how you approach real-world challenges and turn them into actionable insights.
Apply Through Our Website: For the best chance of success, apply directly through our website. This ensures your application gets to the right people and shows us you're serious about joining our team at StudySmarter!
How to prepare for a job interview at Datatech Analytics
✨Know Your Stuff
Make sure you brush up on your data science and applied AI skills, especially in areas like optimisation, forecasting, and NLP. Be ready to discuss specific projects where you've tackled complex systems or worked with imperfect data.
✨Understand the Energy Sector
Familiarise yourself with current trends in the energy and renewables sector. Knowing about flexibility, storage, and climate-informed planning will show that you're not just a tech whiz but also understand the real-world implications of your work.
✨Prepare for Stakeholder Engagement
Since strong stakeholder skills are crucial, think of examples where you've simplified complex information for different audiences. Practice articulating how you can turn analysis into actionable recommendations that drive decisions.
✨Show Your Delivery Mindset
This role is all about delivering solutions, not just theoretical research. Be prepared to discuss how you've built, validated, and iterated models in past roles, and how you’ve partnered with engineering teams to ensure successful deployment.
