At a Glance
- Tasks: Develop alpha-generating signals and pricing models for ETFs and index strategies.
- Company: Dynamic quantitative trading firm focused on systematic models and research-driven decisions.
- Benefits: Collaborative team, minimal meetings, and access to top-notch infrastructure and data.
- Other info: Join a small, senior team where your work directly influences trading decisions.
- Why this job: Make a real impact with your research and see it live in the market.
- Qualifications: Strong quantitative background and 3+ years in systematic trading or quantitative research.
The predicted salary is between 60000 - 80000 £ per year.
My client is a research-driven quantitative trading firm operating continuously across global markets. Their edge comes entirely from systematic models, not intuition, and their researchers own the full stack: hypothesis, signal, backtest, implementation, and live trading. If your work is good, it goes live. If it doesn't perform, you find out why and fix it.
What you'll be working on:
- Developing alpha-generating signals and pricing models for ETF and index-related strategies
- Analysing market microstructure: quote dynamics, liquidity, intraday flows, and passive rebalancing mechanics
- Working with large-scale tick data to identify and exploit structural inefficiencies in exchange-traded products
- Improving execution models and understanding cross-asset hedging relationships
What they are looking for:
- Strong quantitative background (PhD or equivalent research experience in mathematics, statistics, physics, computer science, or a related field)
- 3+ years of applied experience in a systematic trading or quantitative research environment
- Comfort with the full research lifecycle, from data wrangling to live deployment
- Solid programming skills in Python and/or C++; experience with large financial datasets
- Genuine understanding of ETF mechanics, index arbitrage, or market microstructure (not just passing familiarity)
What this is not:
- A sales or structured products role
- A "quant" title with a trader making the calls above you
- A firm where research output gets lost in committee
What you get:
- A small, senior, collaborative team where your research directly determines what they trade. Minimal meetings. High standards. The infrastructure and data to do the work properly.
If you have a meaningful track record in systematic ETF research or market making and want to understand what they're building, reach out.
Quantitative Researcher (ETFs) in Slough employer: Augmentti
As a Quantitative Researcher at our research-driven quantitative trading firm, you will thrive in a dynamic environment where your innovative ideas directly influence trading strategies. We foster a collaborative culture with minimal meetings, allowing you to focus on developing alpha-generating signals and pricing models while benefiting from extensive resources and data. With opportunities for professional growth and a commitment to excellence, we are dedicated to empowering our researchers to take ownership of their work and drive impactful results.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher (ETFs) in Slough
✨Tip Number 1
Network like a pro! Reach out to professionals in the quantitative research field on LinkedIn or at industry events. A personal connection can often get your foot in the door faster than a CV.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those related to ETF strategies or market microstructure. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your programming skills in Python and C++. Practice coding challenges and be ready to discuss your past research experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Quantitative Researcher (ETFs) in Slough
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your quantitative background and programming skills in Python or C++. We want to see how your experience aligns with the full research lifecycle, so don’t hold back!
Be Specific About Your Experience:When detailing your past roles, focus on your applied experience in systematic trading or quantitative research. Share examples of how you've developed models or analysed market microstructure to give us a clear picture of your capabilities.
Demonstrate Your Understanding:We’re looking for candidates who genuinely understand ETF mechanics and market microstructure. Use your application to showcase your knowledge and any relevant projects you’ve worked on that demonstrate this understanding.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and get the ball rolling on your potential future with us.
How to prepare for a job interview at Augmentti
✨Know Your Quantitative Stuff
Make sure you brush up on your quantitative skills and be ready to discuss your research experience in detail. They’ll want to hear about your work with systematic models, so prepare examples that showcase your understanding of the full research lifecycle.
✨Get Familiar with ETFs and Market Microstructure
Dive deep into ETF mechanics and market microstructure concepts. Be prepared to discuss how you’ve applied this knowledge in past roles, especially regarding quote dynamics and liquidity. Showing genuine understanding will set you apart from other candidates.
✨Show Off Your Programming Skills
Since solid programming skills in Python and/or C++ are crucial, be ready to talk about specific projects where you’ve used these languages. If possible, bring along examples of code or algorithms you've developed that relate to trading strategies or data analysis.
✨Demonstrate Problem-Solving Ability
They’re looking for someone who can not only develop models but also troubleshoot when things go wrong. Prepare to discuss instances where you identified issues in your research or trading strategies and how you went about fixing them. This shows you’re proactive and results-driven.
