Senior Quantitative Researcher/ Sub-PM

Alexander Chapman
London
7 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Science Consultant

Senior Data Strategy Consultant, Marketing Solutions

2x Senior Data Engineer (Financial Services)

Senior Data Engineer

Senior Data Engineer

Title: Senior Quantitative Researcher / Sub-Portfolio Manager

Location: New York / London

Team: Systematic Trading Strategies

About the Role:

Seeking a highly skilled and experienced Senior Quantitative Researcher or Sub-Portfolio Manager to join a systematic trading team. The successful candidate will play a key role in the full lifecycle of alpha research and strategy development, with the potential to manage risk capital independently or transition into a lead PM role over time.

Key Responsibilities:

  • Design, research, and implement systematic trading strategies across global equities, futures, FX, or other liquid asset classes
  • Conduct high-quality alpha signal research using alternative data, statistical techniques, and machine learning when appropriate
  • Develop and test robust portfolio construction, execution, and risk management models
  • Collaborate closely with data engineering and infrastructure teams to enhance research platform capabilities
  • Take ownership of strategy performance and contribute to the team’s overall P&L
  • Potential to transition into a standalone PM role or run a sub-portfolio within defined risk limits

Requirements:

  • 5+ years of experience in quantitative research or trading at a hedge fund, proprietary trading firm, or top-tier investment bank
  • Proven track record of alpha generation or contribution to profitable strategies
  • Deep understanding of statistical modeling, time-series analysis, and/or machine learning techniques
  • Strong programming skills in Python, C++, or similar; experience working with large datasets and research infrastructure
  • Master’s or PhD in a quantitative field (e.g., Mathematics, Computer Science, Physics, Engineering, Statistics)
  • Excellent communication skills and ability to work in a collaborative, performance-driven environment

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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.