Cubist Data Scientist

Point72 Careers
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist

ROLE

We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance.


Cubist’s data services group is looking for a Data Analyst to join our dedicated team. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers in the world.


This exceptional individual will be a member of a small team of Data Scientists/Analysts who play a vital role in ensuring the smooth day-to-day implementation of a large research infrastructure, and the live production trading of billions of dollars of capital across global capital markets, including equities, futures, options and other financial instruments.


RESPONSIBILITIES

  • Identifying potential data sources
  • Coordinate with compliance team and legal team on new vendor trial/subscription process
  • Assist with collecting and maintaining overviews and vendor content offering
  • Assist with data questions and requests from investment teams
  • Setting up feed download and monitor check in database
  • Monitoring the automated data collection and cleansing infrastructure
  • Coordinating meetings and conference calls between data users and experts.
  • Assist in organizing presentations
  • Handle user requests and answer questions about data
  • Download trial datasets from vendor FTP sites or other delivery mechanism
  • Assist Data Team with manual data processing as required
  • Maintain Cubist Data Wiki contents

REQUIREMENTS

  • Basic level programming experience in Python and SQL. Experience with AWS and Airflow preferred but not required
  • Financial industry experience preferred but not required
  • Strong organization, communication and interpersonal skills
  • Attention to detail and a love of process
  • Strong oral and written communication skills
  • Ability to exercise sound judgment in assessing and determining how to handle queries, calls and issues
  • Ability to multitask and prioritize assignments


#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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.