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- Tasks: Develop cutting-edge models and analytics for Capital Markets Financing's trading desks.
- Company: Join UBS, a global leader in wealth management and investment banking.
- Benefits: Enjoy competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact in finance with innovative solutions and collaboration across global teams.
- Qualifications: Proficient in Python, financial mathematics, and large-scale data analytics.
- Other info: Inclusive culture with a focus on collaboration and career development.
The predicted salary is between 36000 - 60000 £ per year.
Your role as a member of our London team includes:
- Developing models and analytics for Capital Markets Financing’s (CMF) trading desks in London, with primary focus on index / delta-1 trading and optimisation.
- Delivering high-quality quantitative apps and workflows for CMF Index trading, in partnership with other Quant and IT teams.
- Contributing to the development of innovative marking, rebalancing, and optimisation solutions in collaboration with our wider global team as well as Equity Derivative Quants.
- Building strong relationships with Trading, Quant, IT and Risk Control partners both in London and across regions.
Your expertise should include:
- Proficiency in Python for developing analytics and models.
- Strong knowledge of financial mathematics and optimisation theory, with proficiency in corresponding Python packages.
- Extensive experience in large-scale data analytics pipelines, with at least one of MS Azure Data Bricks, SQL, or KDB.
- Academic background in Mathematics or similar STEM subject.
- Professional experience in trading, quantitative research, or quantitative development teams in Prime Financing, Equity Derivative or Fixed Income Repo divisions.
- Proficiency in additional programming languages, such as C++, or front-end development in React/Enaml.
- Professional experience with Azure workflows and components; streaming workflows is a plus.
- Strong knowledge of AI/ML theory, with experience in delivering such models and pipelines at scale in production a plus.
- Post-graduate academic studies in Mathematics or similar STEM subject.
At UBS, we know that it is our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey.
Financing Quantitative Analyst employer: UBS
Contact Detail:
UBS Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Financing Quantitative Analyst
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works at UBS. Building relationships can open doors that applications alone can't.
✨Show Off Your Skills
When you get the chance to chat with potential employers, don’t hold back! Share your projects, especially those involving Python and financial models. Let them see what you can bring to the table.
✨Tailor Your Approach
Do your homework on UBS and the role of Financing Quantitative Analyst. Tailor your conversations to highlight how your skills in optimisation and data analytics align with their needs. It shows you're genuinely interested!
✨Apply Through Our Website
Don’t forget to apply through the UBS careers site! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining our team.
We think you need these skills to ace Financing Quantitative Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your proficiency in Python, financial mathematics, and any relevant projects you've worked on. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your background aligns with our needs. Don’t forget to mention your experience in trading or quantitative research – we love that stuff!
Showcase Your Projects: If you've worked on any relevant projects, especially those involving large-scale data analytics or AI/ML, make sure to include them. We’re keen to see how you’ve applied your skills in real-world scenarios, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at UBS!
How to prepare for a job interview at UBS
✨Know Your Numbers
Brush up on your financial mathematics and optimisation theory. Be ready to discuss how you've applied these concepts in your previous roles, especially in relation to Python packages. This will show that you not only understand the theory but can also implement it practically.
✨Showcase Your Coding Skills
Prepare to demonstrate your proficiency in Python and any other programming languages you know, like C++. You might be asked to solve a coding problem or explain your thought process behind a project. Practising common algorithms and data structures can really help you shine.
✨Familiarise Yourself with the Tools
Get comfortable with large-scale data analytics pipelines and tools like MS Azure Data Bricks or SQL. If you have experience with Azure workflows, be ready to discuss specific projects where you used these tools to deliver results. This shows you're not just a theorist but someone who can get things done.
✨Emphasise Collaboration
Since collaboration is key at UBS, think of examples from your past experiences where you worked effectively with teams across different functions. Highlight how you built relationships with Trading, Quant, IT, and Risk Control partners, as this will resonate well with their team-oriented culture.
