Quantitative Researcher

Durlston Partners
London
3 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Researcher

Quantitative Researcher

Quantitative Researcher

Quantitative Researcher

Quantitative Researcher

Quantitative Researcher – Trading ML & Forecasting

Junior Quantitative Researcher (HFT / London / hybrid)


Maximise your chances of a successful application to this job by ensuring your CV and skills are a good match.

We are looking for a Quantitative Researcher with strong modelling and coding skills (Python). You will be responsible for scaling and bringing our quantitative business to the next level. You will have the opportunity to cover all technologies (CeFi, DeFi), trading platforms and products (spot, derivatives, ETPs, etc.). You will work with other researchers, traders and developers to build trading strategies and improve existing algorithmic trading activities.

Responsibilities:

  • Design and implement predictive quantitative trading market making as well as taking models.
  • Apply statistical techniques to develop short-term signals, with a time horizon from milliseconds to a few minutes.
  • Lead research efforts to improve signals and optimise parameters through back testing, across a wide range of trading products and technologies.
  • Proactively identify market microstructure patterns and trading opportunities by analysing vast quantities of tick level historical market data across many markets.
  • Run simulations and model market for both liquid and illiquid assets.
  • Improve and maintain supporting infrastructure in Python and C++.

Qualifications:

  • Quantitative degree in Mathematics, Statistics, Computer Science, Physics or related qualitative field. Post-graduate degrees may be a plus but not expected or required.

Required Skills:

  • Advanced Python coding skills.
  • Experience and advanced knowledge of statistics/probability theory.

If you are interested please apply or email at

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.