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- Tasks: Validate ML models in energy trading risk management, ensuring quality and compliance.
- Company: Join a leading firm in energy trading, focused on innovative risk management solutions.
- Benefits: Enjoy remote work flexibility and competitive daily rates based on experience.
- Why this job: Be part of a dynamic team impacting the energy sector with cutting-edge technology.
- 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)
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 or webinars to connect with potential colleagues and learn about the latest trends in model validation and machine learning applications.
✨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 attract attention from recruiters.
✨Tip Number 3
Familiarise yourself with the specific challenges of model validation in energy trading. Research recent case studies or publications that highlight successful validation processes to discuss during interviews.
✨Tip Number 4
Prepare to discuss your understanding of credit risk in energy markets. Be ready to explain how your experience aligns with the requirements of the role, especially regarding data quality and governance in model implementation.
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're 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 listed in the job description.
Showcase Relevant Projects: If you have worked on specific projects related to model validation or energy trading, 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 for any errors or inconsistencies. A polished application reflects your attention to detail, which is crucial in risk management roles.
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.
✨Understand the Energy Market
Demonstrate your deep knowledge of European energy markets and traded instruments. Familiarise yourself with current trends and challenges in the sector, as this will show your genuine interest and expertise.
✨Prepare for Model Validation Questions
Expect questions related to model validation processes, including benchmarking, back-testing, and sensitivity analysis. Be ready to explain your approach to evaluating model implementation and input quality.
✨Communicate Clearly
Since fluency in English is a must, practice articulating your thoughts clearly and concisely. Use examples from your past experiences to illustrate your points, ensuring you convey your understanding effectively.
