Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Quantitative Researcher, Options

Teza Technologies
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Researcher

Treasury Senior Quantitative Researcher

Senior Quantitative Recruitment Consultant

Lead Data Engineer

Senior Project Manager, Quantitative (Remote)

C++ Quantitative Developer - C+/20 | Linux | Low-Latency | Equities | Python - Permanent

We are looking for a Senior Quantitative Researcher, Options at Teza Technologies. The role demands sharp analytical skills, relentless commitment to excellence, and a passion for uncovering hidden patterns in data, with prior experience in options.


Location

London, UK (Hybrid mode with 3 days in-office requirement)


Key Responsibilities

  • Lead the research and development of systematic options trading strategies across US and global markets.
  • Apply advanced options pricing models, volatility surface modeling, and risk‑neutral frameworks to generate alpha.
  • Conduct rigorous backtesting, stress testing, and statistical validation of strategies.
  • Collaborate with technologists to implement research into production‑ready trading systems with robust execution.
  • Enhance portfolio construction and risk management frameworks for options books.
  • Contribute to the evolution of Teza’s options research platform, embedding innovation into live strategies.
  • Mentor junior researchers and drive the continuous improvement of research practices, infrastructure, and tools.

Basic Requirements

  • Physics, Mathematics, Computer Science, Engineering or other technical degree.
  • Strong mathematics skills: statistics, linear algebra, optimization, etc.
  • Minimum of 4 years of quantitative research or trading experience in systematic trading.
  • Deep expertise in options, including volatility surface and derivatives pricing methods.
  • Strong programming skills in Python, with experience handling large, complex datasets.
  • Solid understanding of risk management principles in derivatives trading.
  • Ability to work effectively across research, trading, and technology teams.
  • Exceptional analytical, problem‑solving, and critical‑thinking skills.

Nice to Have Requirements

  • PhD in Physics, Mathematics, Computer Science, Engineering or similar area.
  • Experience deploying systematic options strategies into production trading environments.
  • Familiarity with market microstructure and low‑latency execution in derivatives.
  • Knowledge of machine learning techniques and their application to options trading.
  • Experience mentoring or leading a quant research team.

What You’ll Get

  • On‑site presence of experienced and skilled Portfolio Managers to brainstorm with.
  • Build strategies while becoming the best at what you do, with the potential to run your own desk and become a Portfolio Manager in no time.
  • Access to CIO, CRO and executive team as your advisors.

What Makes You a Match

  • You are a stellar professional at what you do.
  • Difficult problems excite you.
  • You have a lot of passion and drive.

Benefits

  • Health insurance
  • Flexible sick time policy
  • Office lunches


#J-18808-Ljbffr

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.

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.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.