L/S Equities Data Scientist

Point72 Asset Management, L.P
London
1 month ago
Applications closed

Related Jobs

View all jobs

Strategic Finance Analyst

Strategic Finance Manager

Data Engineer

Senior Data Engineer - Microsoft Fabric

Data Engineer

Data Scientist, AI/ML, Associate

A Career in Long/Short Equities at Point72

Long/short equity is Point72’s core investment strategy, and our success is dependent upon our investing teams, comprised of portfolio managers and research analysts.


Summary

As a data analyst embedded in an investing team, you will help drive innovation by leveraging a variety of data sources and systems. You will work directly with a portfolio manager and their team of research analysts to connect data and fundamental research. Your work will support investment theses and help inform investment decisions.


What You’ll Do

  • Work directly with a portfolio manager and a team of research analysts to analyze a broad universe of Compliance-approved data to directly influence idea generation and investment decisions, with an emphasis on equities.
  • Design, build, and maintain data products and predictive models that enable analysis of key performance indicators (KPIs) and forward-looking metrics relevant to your specific sector.
  • Investigate and explore innovative approaches to extract meaningful information from complex datasets.
  • Develop agile, automated systems that rapidly ingest, synthesize, and analyze diverse sets of information in real-time, producing actionable insights to support timely decision-making.
  • Optimize and scale existing data infrastructure to enhance performance, reliability, and adaptability in handling complex, high-volume data streams.
  • Develop and maintain high-quality, efficient, and modular code, creating well-documented custom libraries and data pipelines that enhance the team's analytical capabilities and ensure reproducibility of results.
  • Stay on top of the newest technologies approved for use across the firm.
  • Partner with Compliance on all aspects such as approved data sources, permissibility of systems, and analytics.


What’s Required

  • Undergraduate degree or higher.
  • Quantitative ability as demonstrated through relevant coursework or work equivalent.
  • Experience working with diverse data types including structured, unstructured (e.g., text), and time-series data.
  • Strong understanding of statistical concepts and their application to large-scale data analysis, with an interest in applying these skills to financial datasets.
  • Strong coding skills in a high-level programming language (e.g., Python, R, or similar). Familiarity with version control systems (such as Git) and collaborative development practices.
  • Strong data visualization skills, with experience in creating informative visualizations using coding languages like Python or R.
  • Strong analytical and problem-solving skills, with the ability to approach complex issues systematically and creatively. Demonstrated capacity to translate business questions into data-driven solutions.
  • Team player with an entrepreneurial spirit and good communication skills.
  • Deep intellectual curiosity and lifelong-learning mindset.
  • Commitment to the highest ethical standards.


We take care of our people

We invest in our people, their careers, their health, and their well-being. When you work here, we provide:

  • Private Medical and Dental Insurances
  • Generous parental and family leave policies
  • Volunteer Opportunities
  • Support for employee-led affinity groups representing women, people of colour and the LGBT+ community
  • Mental and physical wellness programs
  • Tuition assistance
  • Non-contributory pension and more


About Point72

Point72 Asset Management is a global firm led by Steven Cohen that invests in multiple asset classes and strategies worldwide. Resting on more than a quarter-century of investing experience, we seek to be the industry's premier asset manager through delivering superior risk-adjusted returns, adhering to the highest ethical standards, and offering the greatest opportunities to the industry's brightest talent. For more information, visit www.Point72.com/about


#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.