Data Scientist | Equity (L/S) Hedge Fund

Selby Jennings
City of London
3 months ago
Create job alert
Data Scientist | Equity (L/S) Hedge Fund

A newly launched long/short hedge fund is seeking a Data Scientist to join its investment team. This is a high-impact role where applied data science is central to generating differentiated insights and driving portfolio performance.

About the Role

You’ll work closely with investors and engineers to source, structure, and analyze real-world datasets, build predictive models, and create outputs that directly inform investment decisions. The role combines data origination with advanced analytics – expect to work with large-scale alternative data, develop KPI forecasting models, and design dashboards that track fundamentals in real time.

Key Responsibilities
  • Originate and evaluate novel datasets (e.g., supply chain, geospatial, IoT, pricing, web activity) and manage onboarding of new vendors.
  • Collaborate with the investment team to translate hypotheses into data-driven projects with measurable impact.
  • Build predictive models for company KPIs using econometrics, statistical methods, and machine learning.
  • Design and maintain dashboards to monitor fundamentals and calibrate investment theses.
  • Work with engineers to integrate models and dashboards into a scalable data platform.
  • Apply AI/ML techniques (e.g., NLP, knowledge graphs) to link and organize datasets across companies and sectors.
Ideal Candidate Profile
  • Strong Python skills (pandas, NumPy, scikit-learn; familiarity with PyTorch/TensorFlow a plus).
  • Proficiency in SQL and experience handling large datasets; Tableau or similar BI tools for dashboards.
  • 3+ years’ experience applying advanced analytics or ML to real-world data, ideally in finance, supply chain, or predictive modeling contexts.
  • Strong quantitative background (Math, Physics, Computer Science, Econometrics, or related fields).
  • Demonstrated ability to source and leverage new datasets, not just standard financials.
  • Excellent communication skills and ability to collaborate across investment and technical teams.
Why This Role Is Exciting

You’ll have direct exposure to the founders, CIO, senior traders, and heads of key functions – giving you unparalleled insight into investment strategy and decision-making. This is a rare opportunity to shape a data science capability from the ground up while working alongside some of the most respected professionals in the industry.

If you feel this is a good match – apply today!

Seniority level

Entry level

Employment type

Full-time

Job function

Engineering, Information Technology, and Research

Industries

Investment Management

Referrals increase your chances of interviewing at Selby Jennings by 2x


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Senior Data Scientist / Machine Learning Engineer

Staff Data Scientist

Data Science Manager – Property Tech – London

Data Science Manager - Property Tech - London

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.

Jobs

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.

Careers

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.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.