At a Glance
- Tasks: Develop and implement systematic trading strategies using advanced data analysis.
- Company: Leading financial 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 with cutting-edge technology.
- Qualifications: Degree in STEM, strong Python skills, 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 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
✨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’s already in the game. We can’t stress enough how valuable personal connections can be when it comes to landing that dream job.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects, especially those involving data analysis and machine learning. We love seeing real-world applications of your skills, so make sure to highlight your best work!
✨Ace the Interview
Prepare for those tricky interview questions by practising your responses. We recommend using the STAR method (Situation, Task, Action, Result) to structure your answers. And don’t forget to ask insightful questions about the role and the team!
✨Apply Through Our Website
When you find a role that excites you, apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who fit our culture.
We think you need these skills to ace Quantitative Researcher, Systematic Equities
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Quantitative Researcher role. Highlight your experience with systematic trading strategies and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Showcase Your Skills:In your application, don't just list your technical skills—show us how you've used them in real-world scenarios. Whether it's Python, machine learning, or data analysis, give us examples that demonstrate your expertise.
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 you can contribute to our team. Keep it concise but impactful—let your personality come through!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining StudySmarter!
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 discuss specific projects where you applied mathematical modelling or regression techniques, and how they contributed to successful outcomes.
✨Prepare for Technical Questions
Expect technical questions that assess your machine learning knowledge and experience. Review key concepts and be prepared to explain your thought process behind model implementation and back testing, especially in a systematic trading context.
✨Communicate Effectively
Since excellent communication skills are crucial, practice explaining complex concepts in simple terms. Think about how you can convey your analytical approach and problem-solving strategies clearly, especially when discussing your experience with cross-functional teams.
