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Quantitative Researcher

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Location: London / New York (Hybrid/Remote options available) Compensation: Highly competitive base salary + performance-linked bonus structure
Overview
We are currently partnering with a premier systematic investment firm to identify an exceptional Machine Learning Quantitative Researcher. In this role, you will join a specialized research team dedicated to developing next-generation quantitative trading strategies.
This position offers the opportunity to leverage extensive computational resources and robust data infrastructure to focus exclusively on greenfield research. You will be tasked with applying advanced machine learning methodologies to complex, unstructured datasets to identify and capture new sources of alpha in global financial markets.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Key Responsibilities
- Design, develop, and deploy advanced machine learning models (including Deep Learning, Reinforcement Learning, and modern sequence models) to forecast asset price volatility and directional movement.
- Conduct rigorous, empirical research across diverse, large-scale datasets to extract commercially viable trading signals.
- Partner closely with senior portfolio managers and quantitative developers to seamlessly transition research models into high-performance, production-ready trading systems.
- Continuously monitor, evaluate, and optimize model performance in live trading environments.
Basic Qualifications
Education
- Ph.D. or Master’s degree in Machine Learning, Computer Science, Physics, Applied Mathematics, Statistics, or a closely related quantitative discipline from a top-tier academic institution.


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Programming Expertise
- Advanced proficiency in Python and C++ within a Linux/UNIX environment.
Technical Stack
- Extensive practical experience with leading machine learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
Research Experience
- A demonstrated track record of designing and implementing complex machine learning architectures to solve rigorous, data-heavy problems. Note: Prior experience within the financial services or quantitative trading sector is not a prerequisite.
Execution
- Strong analytical capabilities with a demonstrated focus on the commercial application and real-world deployment of theoretical models.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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