At a Glance
- Tasks: Develop systematic trading strategies and analyse large financial datasets.
- Company: Leading finance firm with a focus on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Fast-paced environment with excellent career advancement opportunities.
- Why this job: Join a dynamic team and make an impact in the world of finance.
- Qualifications: Degree in STEM, proficient in Python, and experience in quantitative finance.
The predicted salary is between 60000 - 80000 £ per year.
Location: London or Dubai preferred.
Principal Responsibilities
- Work alongside the Senior Portfolio Manager on developing systematic trading strategies, with a primary focus on:
- Idea generation
- Data gathering and analysis
- Model implementation and back testing for systematic global equities strategies
- Explore, analyze, and harness large financial datasets using various statistical learning techniques.
- Work with multiple vendor data sets: assessing, cleaning, creating features.
- Implement flexible, scalable and efficient machine learning framework using existing features.
- Optimize code for larger scale work.
- Create new features using additional database (KDB preferred).
Preferred Technical Skills
- Proficient in modern data science tools stacks (Jupyter, pandas, numpy, sklearn) with machine learning experience.
- Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related STEM field from top ranked University.
- Expert in Python (KDB/Q is a plus).
- Demonstrated knowledge of quantitative finance, mathematical modelling, statistical analysis, regression, and probability theory.
- Excellent communication, problem‐solving, and analytical skills, with the ability to quickly understand and apply complex concepts.
Preferred Experience
- 3+ years of experience working in a systematic trading environment with a focus on equities.
- 3+ years of experience working with multiple vendor data sets and, in particular, manipulating data (assessing, cleaning, creating features, etc.).
- Demonstrated theoretical understanding of Machine Learning with 2-3+ years of hands‐on experience in the applications.
- Experience collaborating effectively with cross functional teams, multitasking and adapting in a fast‐paced environment.
Highly Valued Relevant Attributes
- Strong intuition about feature/data prediction power.
- Extremely rigorous, critical thinker, self‐motivated, detail‐oriented, and able to work independently in a fast‐paced environment.
- Entrepreneurial mindset.
- Curiosity and eagerness to learn and grow professionally.
Quantitative Researcher, Systematic Equities in London employer: Quant Blueprint LLC
As a Quantitative Researcher in Systematic Equities, you will thrive in a dynamic and innovative environment that values collaboration and professional growth. Our London or Dubai offices offer a vibrant work culture, competitive benefits, and the opportunity to work with cutting-edge data science tools while developing impactful trading strategies. Join us to enhance your skills and contribute to a forward-thinking team dedicated to excellence in quantitative finance.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher, Systematic Equities in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data analysis and machine learning. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in quantitative finance. Practice common interview questions and be ready to discuss your past experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure your application stands out!
We think you need these skills to ace Quantitative Researcher, Systematic Equities in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Quantitative Researcher role. Highlight your proficiency in Python, data science tools, and any relevant experience in systematic trading. We want to see how you fit into our world!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about quantitative finance and how your background makes you a great fit for our team. We love seeing enthusiasm and a bit of personality!
Showcase Your Projects:If you've worked on any relevant projects, especially those involving machine learning or data analysis, make sure to mention them. We’re keen to see your hands-on experience and how you’ve tackled real-world problems.
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 don’t miss out on any important updates. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Quant Blueprint LLC
✨Know Your Data Science Tools
Make sure you're well-versed in the modern data science tool stack mentioned in the job description. Brush up on Jupyter, pandas, numpy, and sklearn, and be ready to discuss how you've used these tools in your previous roles.
✨Showcase Your Quantitative Skills
Prepare to demonstrate your understanding of quantitative finance and statistical analysis. Be ready to explain complex concepts like regression and probability theory in a way that shows you can apply them practically in a trading environment.
✨Highlight Your Experience with Data Sets
Since working with multiple vendor data sets is crucial for this role, come prepared with examples of how you've assessed, cleaned, and created features from large datasets. Specific anecdotes will help illustrate your hands-on experience.
✨Emphasise Your Problem-Solving Abilities
Be ready to discuss past challenges you've faced in a fast-paced environment and how you tackled them. Highlight your critical thinking and analytical skills, as well as your ability to work independently while still collaborating effectively with teams.
