At a Glance
- Tasks: Build ML-driven systems from financial data to create actionable investor signals.
- Company: Dynamic fintech startup focused on innovation and real-world impact.
- Benefits: Competitive salary, equity options, and a collaborative in-office environment.
- Other info: Fast-paced team culture with real ownership and growth opportunities.
- Why this job: Directly influence how investors make decisions using cutting-edge AI and finance.
- Qualifications: 5+ years in quant, ML, or financial modelling with strong Python skills.
The predicted salary is between 110000 - 200000 £ per year.
You think in time series, signals, and regimes. You care about insight quality, not academic purity. You want your models tested by markets, not papers. If you dislike messy data and real-world constraints, this is not your role.
The Role, In Plain English
You will build quantitative and ML-driven insight systems using structured time series data. This role exists to turn raw financial data into actionable investor signals. You will work closely with engineers to productionize quant logic.
What You’ll Be Responsible For
- Develop models using structured financial time series
- Build insight generation and scenario analysis pipelines
- Collaborate with backend engineers to deploy models in production
- Evaluate signals based on real investor outcomes
- Improve attribution and explainability
What “Good” Looks Like in This Role
- After 3 months: Shipping signals used internally.
- After 6 months: Signals used by customers.
- After 12 months: You shape how quant insights are built at Reflexivity.
Who You Are (Must-Haves)
- 5 plus years experience in quant, ML, or financial modeling
- Strong Python skills
- Startup experience on core systems
- Investment domain knowledge
- AI-assisted coding experience
Nice-to-Haves (Not Deal Breakers)
- Prior buy-side or sell-side experience
- Experience with alternative data
How We Work
- In-office team with high trust and high ownership
- Direct communication, minimal process, strong opinions backed by data
- Engineers are expected to think about product impact, not just code
- We move fast when it matters and slow down when correctness matters more
Why This Role Is Worth Your Time
- Direct influence on how professional investors make decisions
- Hard problems at the edge of AI, data, and finance
- Real ownership and technical autonomy
- Senior peers who care about quality and outcomes
Compensation & Practicalities
- Base salary: £110,000 to £200,000 depending on experience
- Equity included
- In-office role based in London
- No agency candidates
Machine Learning and Quant Engineer - London employer: Reflexivity
At Reflexivity, we pride ourselves on being an exceptional employer that fosters a culture of high trust and ownership, where your contributions directly influence how professional investors make decisions. Located in the heart of London, we offer competitive compensation, equity options, and the opportunity to tackle challenging problems at the intersection of AI, data, and finance, all while working alongside senior peers who are committed to quality and impactful outcomes.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning and Quant Engineer - London
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and tech space, especially those who are already working in quant or ML roles. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best models and insights. Use platforms like GitHub to share your work, and make sure it’s easy for potential employers to see what you can do with structured financial time series data.
✨Tip Number 3
Prepare for interviews by brushing up on real-world applications of your models. Be ready to discuss how you've tackled messy data and turned it into actionable insights. We want to hear about your hands-on experience!
✨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 the initiative to connect directly with us.
We think you need these skills to ace Machine Learning and Quant Engineer - London
Some tips for your application 🫡
Show Your Passion for Data:When you're writing your application, let us see your enthusiasm for working with messy data and real-world constraints. Share examples of how you've tackled challenges in the past and turned raw data into actionable insights.
Highlight Relevant Experience:Make sure to emphasise your 5+ years of experience in quant, ML, or financial modelling. We want to know about your strong Python skills and any startup experience you have, so don’t hold back on those details!
Be Clear and Concise:We appreciate straightforward communication. Keep your application clear and to the point, focusing on your achievements and how they relate to the role. Avoid jargon unless it’s relevant to the job description.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Reflexivity
✨Know Your Data
Make sure you understand the intricacies of structured financial time series data. Be ready to discuss how you've handled messy data in the past and how you can turn it into actionable insights. This will show your practical experience and readiness for real-world challenges.
✨Showcase Your Python Skills
Prepare to demonstrate your Python expertise, especially in relation to machine learning and quantitative modelling. Bring examples of projects where you've used Python to build models or pipelines, and be ready to explain your thought process and the impact of your work.
✨Discuss Real-World Applications
Be prepared to talk about how your models have been tested in real markets rather than just theoretical scenarios. Highlight any experience you have with evaluating signals based on actual investor outcomes, as this aligns perfectly with what they’re looking for.
✨Emphasise Collaboration
Since you'll be working closely with engineers, it's crucial to convey your ability to collaborate effectively. Share examples of how you've worked in cross-functional teams to deploy models in production, and how you’ve contributed to improving product impact.
