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- Tasks: Lead the design of advanced forecasting models using deep learning for a dynamic energy marketplace.
- Company: Join an AI-for-good startup revolutionising renewable energy access in the UK.
- Benefits: Enjoy flexible remote work, wellbeing allowances, and two fully-paid retreats annually.
- Why this job: Make a real impact in clean energy while mentoring a growing team in a collaborative culture.
- Qualifications: 8-11 years in time series forecasting with deep learning expertise; mentoring experience preferred.
- Other info: Diversity is celebrated here; all backgrounds are encouraged to apply.
The predicted salary is between 48000 - 84000 £ per year.
We are hiring for an AI-for-good startup based in London that is reimagining the future of renewable energy in the UK. Fresh off a £10M+ Series A round in April 2024, they are using cutting-edge AI to connect users directly with renewable energy generators, making clean energy more accessible, efficient, and impactful. With an eye toward a Series B raise in late 2025 or early 2026, they are building out their team to support ambitious growth and deeper market penetration.
The Role
They are looking for a Lead Data Scientist to architect a next-generation, deep learning-based time series forecasting engine. This engine will sit at the core of their dynamic energy marketplace, shaping critical decisions across pricing, risk, and broader commercial strategy. This is a high-impact, high-ownership role with clear leadership opportunities. As the company scales, the data science function will grow under your guidance. You will work closely with a team of 16 engineers who will handle data preparation, freeing you up to focus on advanced modeling and experimentation.
What You Will Do
- Design and build robust long-term forecasting models using deep learning techniques (LSTMs, RGUs, deep fusion models), though strong statistical or machine learning backgrounds are also welcome.
- Take a contextual approach to forecasting, with a focus on intelligent modeling over data wrangling (datasets are already clean and accessible).
- Prepare for a potential future shift toward short-term forecasting as the platform evolves, though long-term remains the current priority.
- Mentor and support junior data scientists as the team expands.
What You Will Need To Bring
- 8 to 11 years of experience working with time series forecasting; long-term forecasting is a plus, but strong short-term expertise is also valuable.
- Proven track record using deep learning for time series forecasting.
- Comfortable collaborating across teams and clearly communicating complex ideas.
- Experienced in mentoring junior team members.
- Nice to have: background in startups or small, fast-moving teams.
- Nice to have: product-focused mindset and familiarity with the energy sector or pricing models.
What They Offer
- Fully remote or London-based (small office space available).
- Flexible working with core hours from 10 AM to 4 PM UK time.
- Mission-driven, collaborative culture with strong team values.
- Wellbeing allowance and two fully-paid company retreats each year.
- Competitive compensation and more, as the company grows.
At Adamas Knight, we are committed to creating an inclusive culture. We do not discriminate based on race, religion, gender, national origin, sexual orientation, age, veteran status, disability, or any other legally protected status. Diversity is highly valued, and we encourage applicants from all backgrounds to apply.
Contact Detail:
Adamas Knight Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist (London Area)
✨Tip Number 1
Familiarise yourself with the latest advancements in deep learning techniques, especially those related to time series forecasting. Being able to discuss recent developments or case studies during your interview can demonstrate your passion and expertise in the field.
✨Tip Number 2
Showcase your collaborative skills by preparing examples of how you've successfully worked with cross-functional teams in the past. This role requires clear communication, so being able to articulate your experiences will set you apart.
✨Tip Number 3
If you have experience mentoring junior data scientists, be ready to share specific instances where you guided them through complex projects. Highlighting your leadership abilities will align well with the high-impact nature of this role.
✨Tip Number 4
Research the renewable energy sector and understand the challenges it faces. Being knowledgeable about the industry will not only help you in interviews but also show your genuine interest in the company's mission and goals.
We think you need these skills to ace Lead Data Scientist (London Area)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with time series forecasting and deep learning techniques. Use specific examples that demonstrate your ability to design and build forecasting models, as well as any mentoring experience you have.
Craft a Compelling Cover Letter: In your cover letter, express your passion for renewable energy and AI. Discuss how your background aligns with the company's mission and how you can contribute to their growth. Mention any relevant experience in startups or fast-paced environments.
Showcase Your Technical Skills: Be explicit about your technical skills related to deep learning and time series forecasting. Include any relevant projects or achievements that demonstrate your expertise in these areas, especially if they relate to the energy sector.
Prepare for Interviews: Anticipate questions about your experience with deep learning and time series forecasting. Be ready to discuss your approach to mentoring junior team members and how you would lead the data science function as the company scales.
How to prepare for a job interview at Adamas Knight
✨Showcase Your Deep Learning Expertise
Be prepared to discuss your experience with deep learning techniques, particularly in time series forecasting. Highlight specific projects where you've successfully implemented models like LSTMs or RGUs, and be ready to explain the impact of your work.
✨Demonstrate Collaborative Skills
Since the role involves working closely with engineers and mentoring junior data scientists, emphasise your ability to collaborate across teams. Share examples of how you've communicated complex ideas effectively and supported team members in their growth.
✨Understand the Energy Sector
Familiarise yourself with the renewable energy landscape and pricing models. Showing that you have a grasp of the industry will demonstrate your commitment and help you connect your technical skills to the company's mission.
✨Prepare for Leadership Questions
As this is a high-impact role with leadership opportunities, be ready to discuss your vision for building and guiding a data science team. Think about your mentoring style and how you plan to foster a collaborative and innovative environment.
