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- Tasks: Validate ML models in energy trading risk and assess their implementation and quality.
- Company: Join a leading firm in energy trading risk management with a focus on innovation.
- Benefits: Enjoy remote work flexibility and a competitive contract for 12 months.
- Why this job: Be part of a dynamic team making an impact in the energy sector with cutting-edge technology.
- Qualifications: Experience with Gradient Boosting and Random Forest models, strong Python skills, and credit risk knowledge required.
- Other info: Fluency in English at C1 level or above is essential.
The predicted salary is between 48000 - 72000 £ per year.
Risk Quant Model Validation in Energy Trading Risk Management
Location: England and Germany (remote)
Contract Duration: 12 month initial contract
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.
- 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
- Model validation within an energy trading risk function
Requirements:
- 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
- Fluent in English (C1 level or above)
Field Management - Risk employer: Cititec
Contact Detail:
Cititec Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Field Management - Risk
✨Tip Number 1
Familiarise yourself with the specific machine learning models mentioned in the job description, such as Gradient Boosting and Random Forest. Having hands-on experience and being able to discuss these models in detail during an interview will set you apart from other candidates.
✨Tip Number 2
Brush up on your Python skills, especially in relation to data manipulation and model validation. Being able to demonstrate your coding abilities through practical examples or even a small project can showcase your expertise effectively.
✨Tip Number 3
Understand the nuances of credit risk within energy and commodities markets. Being able to articulate how this knowledge applies to model validation will show that you not only have technical skills but also a solid grasp of the industry context.
✨Tip Number 4
Prepare to discuss your experience with benchmarking, back-testing, and sensitivity analysis. Having concrete examples ready will help you demonstrate your practical understanding of these concepts and how they relate to the role.
We think you need these skills to ace Field Management - Risk
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning models, particularly Gradient Boosting and Random Forest. Include specific examples of your work in energy trading risk management and any relevant projects.
Craft a Strong Cover Letter: In your cover letter, emphasise your hands-on experience with model validation and your understanding of credit risk in energy markets. Explain why you are interested in this role and how your skills align with the company's needs.
Showcase Technical Skills: Clearly outline your Python skills and any relevant tools or libraries you have used for model validation and testing. Mention any experience with benchmarking, back-testing, and sensitivity analysis to demonstrate your technical expertise.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. Ensure that your English proficiency is evident throughout your documents, as fluency is a requirement for this position.
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 Trading Landscape
Familiarise yourself with the energy trading risk management sector. Being able to discuss current trends, challenges, and regulatory aspects will demonstrate your genuine interest and knowledge in the field.
✨Prepare for Technical Questions
Expect questions related to model validation, testing frameworks, and data quality. Brush up on your Python skills and be ready to explain your approach to evaluating model implementation and conducting stress tests.
✨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, ensuring you convey your expertise effectively.
