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For Companies At a Glance
- Tasks: Design and deploy advanced deep learning models for global energy markets.
- Company: Nanook, a leading energy-focused investment firm with a dynamic team.
- Benefits: Competitive salary, bonuses, healthcare, gym membership, and pension contributions.
- Why this job: Make a real impact on energy trading decisions using cutting-edge technology.
- Qualifications: Advanced degree in a quantitative field and 4+ years of deep learning experience.
- Other info: Join a supportive team with exciting career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
About Nanook
Founded in 2008, Nanook is an energy‑focused investment firm built by experienced energy professionals. Since launching our first fund in 2014, we have applied deep fundamental modelling of energy supply and demand to some of the most complex energy markets in the world. Today, Nanook is a specialist team solving highly complex, real‑world problems in energy trading, building cutting‑edge quantitative and technical solutions where rigorous analysis and disciplined thinking are critical to success.
Role description
We are looking for a Deep Learning Engineer to help design, develop, and deploy advanced deep learning models at Nanook. You will tackle some of the most complex problems in global energy markets, applying cutting‑edge neural network and deep learning techniques across diverse and challenging data sets. The ideal candidate combines deep technical expertise in neural networks and deep learning with practical experience delivering models end‑to‑end, from research to production, in a high‑performance environment. Your work will directly impact trading decisions, turning sophisticated models into actionable insights.
Responsibilities
Essential Skills and Experience
- Advanced degree in a quantitative field.
- 4+ years of hands‑on experience building and shipping deep learning models in production, including ownership of end‑to‑end pipelines.
- Strong proficiency with Python and modern DL frameworks (preferably PyTorch; familiarity with JAX or TensorFlow a plus).
- Solid understanding of statistics and ML fundamentals.
- Experience of supervised learning and regression problems.
- Proficiency with GPUs and efficient training/inference for out‑of‑memory datasets.
- Strong data engineering fluency: experience working with large, messy datasets; familiarity with distributed compute.
- Ability to communicate clearly with both technical and commercial stakeholders.
- Ownership mindset with a track record of delivering measurable impact under time constraints.
- Ability to recognise when simpler approaches are more appropriate.
Desirable Skills and Experience
- Experience producing probabilistic and distributional forecasts.
- Experience with any of the following:
- Attention mechanisms and transformer based architectures.
- Physics informed neural networks.
- Bayesian neural networks.
- Spatiotemporal and/or graph‑based modelling.
- Generative models.
- Reinforcement learning.
- Time series forecasting.
Benefits
- Competitive salary with bonus opportunities
- Life insurance of 4x salary
- Healthcare
- 8% Pension contribution
- Gym Membership
- Cycle to work scheme
- Employee assistance program
- Work socials & trips
- Amazing team & working environment
Deep Learning Engineer employer: NANOOK ENERGY ADVISORS LLP
Contact Detail:
NANOOK ENERGY ADVISORS LLP Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the energy and deep learning sectors on LinkedIn. Join relevant groups and participate in discussions to get your name out there. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your deep learning projects. Whether it's GitHub repos or a personal website, let us see your work in action. This is your chance to demonstrate your expertise and make a lasting impression.
✨Tip Number 3
Prepare for those interviews! Research Nanook and understand their approach to energy markets. Be ready to discuss how your experience aligns with their needs. We want to hear how you can tackle complex problems and deliver impactful solutions.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative. Don’t hesitate – get your application in and let’s make some waves in the energy sector together!
We think you need these skills to ace Deep Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Deep Learning Engineer role. Highlight your experience with deep learning models, Python, and any relevant frameworks like PyTorch. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about deep learning and how your background makes you a great fit for Nanook. Don’t forget to mention any specific projects that showcase your expertise.
Showcase Your Projects: If you've worked on any interesting deep learning projects, make sure to include them in your application. We love seeing real-world applications of your skills, especially if they relate to energy markets or complex datasets!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at NANOOK ENERGY ADVISORS LLP
✨Know Your Deep Learning Stuff
Make sure you brush up on your deep learning knowledge, especially around neural networks and frameworks like PyTorch. Be ready to discuss your past projects in detail, focusing on the end-to-end process of building and deploying models.
✨Show Off Your Problem-Solving Skills
Prepare to tackle some complex energy market problems during the interview. Think about how you've approached similar challenges in the past and be ready to explain your thought process and the impact of your solutions.
✨Communicate Like a Pro
Since you'll need to interact with both technical and commercial stakeholders, practice explaining your work in simple terms. Being able to convey complex ideas clearly will show that you can bridge the gap between tech and business.
✨Demonstrate Ownership and Impact
Highlight instances where you've taken ownership of projects and delivered measurable results. Discuss how you managed time constraints and made decisions about when to use simpler approaches, as this shows your practical experience and strategic thinking.
