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: Enjoy real ownership and work alongside passionate senior peers.
- Why this job: Directly influence investment decisions and tackle challenging AI and finance problems.
- 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 innovation and collaboration. Our London-based team thrives in a dynamic environment where your contributions directly influence how professional investors make decisions, offering you real ownership and technical autonomy. With a focus on employee growth, we provide opportunities to tackle challenging problems at the intersection of AI, data, and finance, all while enjoying competitive compensation and equity options.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning and Quant Engineer - London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the finance and tech scene. Attend meetups, conferences, or even casual coffee chats. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best models and projects. Use platforms like GitHub to share your code and insights. This way, potential employers can see your work in action and get a feel for your expertise.
✨Tip Number 3
Prepare for interviews by brushing up on real-world scenarios. Be ready to discuss how you've tackled messy data and turned it into actionable insights. Employers love hearing about your hands-on experience and problem-solving skills.
✨Tip Number 4
Apply through our website! We want to see your application directly. It shows you're genuinely interested in joining us at StudySmarter and helps us keep track of your journey. Plus, it’s a great way to stand out from the crowd!
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:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on what makes you a great fit for this role. Remember, we’re looking for insight quality over academic purity!
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 it gets into the right hands quickly. Plus, it shows you’re keen to join our team!
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
Since strong Python skills are a must-have, prepare to demonstrate your coding abilities. Bring examples of previous projects where you've built models or pipelines. If you can, walk through your thought process and the impact your work had on the project.
✨Discuss Real Investor Outcomes
Be prepared to talk about how you've evaluated signals based on actual investor outcomes. Share specific examples of how your models have influenced decision-making in a financial context. This will highlight your focus on insight quality over academic purity.
✨Emphasise Collaboration
This role involves working closely with engineers, so be ready to discuss your experience in collaborative environments. Talk about how you've worked with cross-functional teams to deploy models in production and the importance of direct communication in achieving successful outcomes.
