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
Reflexivity is an exceptional employer for Backend Software Engineers, offering a dynamic work environment in London where innovation meets finance. With a strong emphasis on technical ownership and a high-performance culture, employees are empowered to make impactful decisions that drive investor outcomes. The company fosters growth through mentorship and collaboration with AI and product teams, ensuring that every engineer has the opportunity to influence architecture and enhance their skills in a supportive, trust-based atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning and Quant Engineer - London
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We think you need these skills to ace Machine Learning and Quant Engineer - London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Reflexivity, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Reflexivity. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Reflexivity
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Reflexivity!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
