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- Tasks: Validate ML models for 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 daily rates based on experience.
- Why this job: Be part of an innovative team shaping the future of energy trading with impactful work.
- Qualifications: 5+ years in model validation, strong Python skills, and expertise in 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 team. 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)
Risk Quant employer: Cititec
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, particularly Gradient Boosting and Random Forest. Engage in discussions on platforms like LinkedIn or relevant forums to demonstrate your expertise and stay updated on the latest trends in model validation.
✨Tip Number 3
Familiarise yourself with the specific challenges and regulations in European energy markets. Understanding the nuances of credit risk in this context will help you stand out as a candidate who is not only technically skilled but also knowledgeable about the industry landscape.
✨Tip Number 4
Prepare for potential interviews by brushing up on your Python skills and being ready to discuss your previous model validation experiences. Be prepared to explain your approach to evaluating model implementation and input quality, as these are key aspects of the role.
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 grammatical errors or typos, 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
Make sure to highlight your hands-on experience with machine learning models, especially Gradient Boosting and Random Forest. Be prepared to discuss specific projects where you've implemented these models and the outcomes they produced.
✨Demonstrate Industry Knowledge
Familiarise yourself with the European energy markets and traded instruments. During the interview, reference current trends or challenges in the market to show that you are well-informed and engaged with the industry.
✨Prepare for Model Validation Questions
Expect questions related to model validation processes, including documentation, testing frameworks, and benchmarking. Be ready to explain your approach to evaluating model implementation and input quality, as this is crucial for the role.
✨Communicate Clearly and Confidently
Since fluency in English is a requirement, practice articulating your thoughts clearly. Use examples from your past experiences to illustrate your points, and don't hesitate to ask for clarification if you don't understand a question.
