At a Glance
- Tasks: Join a dynamic team to develop statistical models using Python and SQL.
- Company: A boutique systematic hedge fund focused on innovation and agility in trading.
- Benefits: Enjoy a competitive salary, bonuses, and a collaborative work environment.
- Other info: Opportunity for personal and professional growth in a non-bureaucratic setting.
- Why this job: Work closely with elite technologists and researchers in a fast-paced, supportive culture.
- Qualifications: MSc or PhD in a STEM subject; strong programming skills in Python and SQL required.
The predicted salary is between 43200 - 72000 £ per year.
Contact Details:
Oxford Knight Recruitment Team
rosie.griggs@oxfordknight.co.uk
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Analyst - Systematic Hedge Fund- Python | C# | SQL | Machine Learning in London
✨Tip Number 1
Network with professionals in the quant finance space. Attend industry meetups, webinars, or conferences where you can connect with current Quantitative Analysts or recruiters. This can give you insights into the role and potentially lead to referrals.
✨Tip Number 2
Brush up on your programming skills, especially in Python and SQL. Consider working on personal projects or contributing to open-source projects that involve data analysis or machine learning to showcase your abilities.
✨Tip Number 3
Familiarise yourself with the latest trends in quantitative finance and machine learning. Read relevant research papers or follow influential figures in the field on social media to stay updated and demonstrate your passion during interviews.
✨Tip Number 4
Prepare for technical interviews by practising problem-solving and coding challenges related to statistical modelling and data analysis. Websites like LeetCode or HackerRank can be great resources for honing your skills.
We think you need these skills to ace Quantitative Analyst - Systematic Hedge Fund- Python | C# | SQL | Machine Learning in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your MSc or PhD in a STEM subject, along with your programming experience in Python and SQL. Emphasise any relevant projects or roles that showcase your skills in time-series analysis, model development, and visualisation.
Craft a Compelling Cover Letter:In your cover letter, express your enthusiasm for the role and the company. Mention how your background aligns with their focus on agility and technology-driven solutions. Include specific examples of your experience with statistical models and large data sets.
Showcase Relevant Skills:When detailing your experience, be sure to highlight your object-oriented programming skills and any familiarity with optimisation techniques. Discuss your ability to work collaboratively with software engineers and quant researchers, as this is crucial for the role.
Proofread and Edit:Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. A polished application reflects your attention to detail, which is essential for a Quantitative Analyst position.
How to prepare for a job interview at Oxford Knight
✨Showcase Your Technical Skills
Make sure to highlight your programming experience in Python and SQL during the interview. Be prepared to discuss specific projects where you've used these languages, especially in relation to statistical modelling or data analysis.
✨Demonstrate Your Analytical Thinking
Quantitative Analysts need strong analytical skills. Prepare to solve a case study or a technical problem during the interview. This will showcase your ability to think critically and apply your knowledge to real-world scenarios.
✨Familiarise Yourself with Time-Series Analysis
Since the role involves time-series analysis and forecasting models, brush up on these concepts. Be ready to explain how you would approach building a forecasting model and discuss any relevant experience you have in this area.
✨Emphasise Team Collaboration
Given the small, agile environment of the firm, it's important to demonstrate your ability to work well in a team. Share examples of past collaborations with software engineers or researchers, highlighting how you contributed to successful outcomes.
