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- Tasks: Lead the design and deployment of ML models for trading and investment platforms.
- Company: Leading investment bank in the heart of London with a focus on financial innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborate with top traders and researchers in a dynamic, high-impact environment.
- Why this job: Make a real impact in finance using cutting-edge machine learning technologies.
- Qualifications: 7+ years in quant/ML roles, advanced degree, and expert programming skills in Python.
The predicted salary is between 72000 - 108000 £ per year.
Senior Quant Machine Learning Engineer sought by leading investment bank based in the city of London.
Inside IR35, 4 days a week on site
To lead the design and deployment of ML-driven models across our trading and investment platforms. This is a high-impact, front-office role offering direct collaboration with traders, quant researchers, and technologists at the forefront of financial.
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Machine Learning Quant Engineer - Investment banking / XVA employer: Harvey Nash
Contact Detail:
Harvey Nash Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Quant Engineer - Investment banking / XVA
✨Tip Number 1
Network like a pro! Reach out to your connections in the finance and tech sectors. Attend industry events or webinars where you can meet potential employers and showcase your expertise in ML and quant engineering.
✨Tip Number 2
Prepare for technical interviews by brushing up on your ML knowledge and coding skills. Practice solving problems related to financial markets and be ready to discuss your past projects and how they relate to the role you're applying for.
✨Tip Number 3
Showcase your passion for ML and finance by contributing to open-source projects or writing articles on platforms like Medium. This not only builds your portfolio but also demonstrates your commitment to staying current in the field.
✨Tip Number 4
Don’t forget to apply through our website! We’re always looking for talented individuals like you, and applying directly can sometimes give you an edge over other candidates. Plus, it’s super easy!
We think you need these skills to ace Machine Learning Quant Engineer - Investment banking / XVA
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Machine Learning Quant Engineer. Highlight your experience with ML techniques and financial markets, and don’t forget to showcase any relevant projects or publications.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about this role and how your background makes you a perfect fit. Be specific about your experience in quant roles and ML applications.
Showcase Your Technical Skills: Since this role requires strong programming skills, make sure to mention your expertise in Python and any other relevant technologies. If you've deployed ML models before, share those experiences to demonstrate your hands-on capabilities.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s quick and easy, and we can’t wait to see what you bring to the table!
How to prepare for a job interview at Harvey Nash
✨Know Your ML Models Inside Out
Make sure you can discuss the ML models you've worked on in detail. Be prepared to explain your design choices, the algorithms you used, and how they impacted the trading or investment strategies. This shows your depth of knowledge and ability to apply theory to practice.
✨Brush Up on Financial Markets
Since this role is in investment banking, having a solid understanding of financial markets is crucial. Familiarise yourself with different asset classes and how they interact. Being able to discuss current market trends and their implications will impress your interviewers.
✨Showcase Your Collaboration Skills
This position involves working closely with traders and other teams. Prepare examples of past collaborations where you successfully communicated complex ML concepts to non-technical stakeholders. Highlighting your teamwork skills will demonstrate that you can thrive in a cross-functional environment.
✨Stay Updated on Cutting-Edge Research
The field of machine learning is constantly evolving. Make sure you're aware of the latest advancements, especially those relevant to finance, like reinforcement learning or generative models. Mentioning recent papers or breakthroughs during your interview can set you apart as a candidate who is genuinely passionate about the field.