Front Office Full Stack Developer Apprentice

  • Level 4-7 (Higher)
  • London

Morgan Stanley

The Macro Tools Quantitative Analysts team is a diverse and highly technically skilled global team within Morgan Stanley’s Fixed Income Division. The team is responsible for the development and design of cutting edge tools which allow the sales and trading desks to closely monitor market movements, intraday risk & pnl (profit and loss), as well as conducting in-depth data analysis and research into trading strategies.

A typical day

  • *Start date: September 2021*
  • You will work closely with the development team and our end users, learning about a range of instruments and asset classes
  • Work on design and technical requirements discussion with development team
  • Improving and reviewing existing capabilities and processes
  • Exploring data via python, Jupyter Notebooks and q/kdb+
  • Front end: UI development using html, JavaScript and AngularJS framework
  • Regular collaboration with wider development team
  • You will also be exposed to a variety of programming languages, libraries and internal systems, providing great leverage for development and to enhance existing tooling

You must have

  • Five GCSE grades C – A* (4-9) or equivalent, including English and Maths
  • Level 3 qualification (A level, BTEC or equivalent)
  • 3 A levels at grades A*-C (or 240 UCAS points or new tariff) – focused on STEM

Skills needed

Problem solving
  • An interest in computer programming, algorithm design and problem solving
  • An inquisitive mindset, keen to learn
  • Familiar with python, JavaScript, Excel, git would be helpful
  • Results oriented, driven and eager to succeed
  • A high-level of initiative and self-motivation
  • Organised with strong attention to detail
  • An ability to build and maintain relationships within the team
  • Strong communication skills (written and verbal)
  • Ability to multi-task and handle working in a fast-paced environment
  • An ability to manage projects, daily responsibilities, and ensure accurate production of analytics