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- Tasks: Validate ML models in energy trading risk and assess their performance.
- Company: Join a leading firm in energy trading risk management with a global presence.
- Benefits: Enjoy remote work flexibility and competitive pay based on experience.
- Why this job: Be part of an innovative team making a real impact in the energy sector.
- Qualifications: 5+ years in model validation, strong Python skills, and knowledge of energy markets required.
- Other info: Initial 12-month contract with potential for extension.
The predicted salary is between 60000 - 84000 £ per year.
Model Validation in Energy Trading Risk Management
Location: England and Germany (remote)
Contract: 12 month initial contract, £ per day DOE
One of our clients is looking for a contractor to join their energy trading risk management. The focus is on validating ML models used in energy trading risk. This includes:
- Reviewing model documentation and theoretical foundations
- Evaluating model implementation and input quality (automation, governance, data quality)
- Assessing testing frameworks
- Conducting benchmarking, back-testing, sensitivity analysis & stress testing
- Reviewing monitoring approaches
- Documenting validation outcomes
Must-Have Experience:
- Model validation within an energy trading risk function
- Hands-on experience with machine learning models (Gradient Boosting (GBM) and Random Forest (RF) are mandatory)
- Strong Python skills
- Solid understanding of credit risk in the context of energy/commodities markets
- At least 5 years' experience in modelling and model validation
- Deep knowledge of European energy markets and traded instruments
- Fluent in English (C1 level or above)
Contact Detail:
Cititec Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Risk Quant
✨Tip Number 1
Network with professionals in the energy trading sector. Attend industry conferences, webinars, or local meetups to connect with people who work in model validation and risk management. This can lead to valuable insights and potential referrals.
✨Tip Number 2
Showcase your hands-on experience with machine learning models by discussing relevant projects during interviews. Be prepared to explain your approach to model validation, particularly with Gradient Boosting and Random Forest techniques, as these are crucial for the role.
✨Tip Number 3
Stay updated on the latest trends and regulations in European energy markets. Understanding current market dynamics will not only enhance your discussions but also demonstrate your commitment to the field during interviews.
✨Tip Number 4
Prepare to discuss your experience with credit risk in energy/commodities markets. Be ready to provide examples of how you've assessed risks and validated models in previous roles, as this will be a key focus for the hiring team.
We think you need these skills to ace Risk Quant
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in model validation, particularly within energy trading risk. Emphasise your hands-on experience with machine learning models like Gradient Boosting and Random Forest, as well as your strong Python skills.
Craft a Compelling Cover Letter: In your cover letter, explain why you are a great fit for the Risk Quant position. Discuss your understanding of credit risk in energy markets and how your background aligns with the requirements outlined in the job description.
Showcase Relevant Projects: If you have worked on specific projects related to model validation or energy trading risk, include these in your application. Detail your role, the challenges faced, and the outcomes achieved to demonstrate your expertise.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any spelling or grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail.
How to prepare for a job interview at Cititec
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with machine learning models, particularly Gradient Boosting and Random Forest. Bring examples of past projects where you validated models in energy trading risk, as this will demonstrate your expertise.
✨Understand the Energy Market
Brush up on your knowledge of European energy markets and traded instruments. Being able to discuss current trends or recent changes in the market can set you apart and show your genuine interest in the field.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would approach model validation challenges, such as data quality issues or testing frameworks, and be ready to articulate your thought process.
✨Communicate Clearly and Confidently
Since fluency in English is a must, practice articulating your thoughts clearly. Use technical language appropriately but ensure that you can explain complex concepts in simple terms, as this shows your depth of understanding.
