Morgan Stanley 2020 Quantitative Finance Systematic Trading Off-cycle Programme (London),UK

2019-12-16 MorganStanley Morgan Stanley

Master's Degree, Doctorate Degree
Quantitative Finance


Quantitative Finance Associate Strategists are placed in a strategists (‘strats’) team related to their specialism, but will typically be partnered with particular business lines or desks to work on specific projects or models. This role is in the Institutional Equities Division within the Strategists team. You can expect to take on significant responsibility as soon as you start. You will work directly with desks at a senior level, applying your skills and subject-matter expertise, to help them make strategic decisions, develop quantitative edge, drive efficiencies and effect changes.


Training includes on-the-job training and one-on-one sessions to familiarize you with the Firm's data resources and analytical tools. The curriculum covers market knowledge and product and technical training. Throughout the Program, you will be continually exposed to management, and you will benefit from networking opportunities with peers and colleagues. You will also be assigned a mentor to ease your transition into the corporate environment by offering career guidance, serving as a sounding board, and helping connect you to the broader Morgan Stanley network. Your co-workers will include motivated experienced industry leaders as well as graduates from top Universities that enjoy solving interesting problems in a collaborative environment.


The equity systematic trading team drives the firm’s automated liquidity and risk product offering for our clients. As part of the team you will be working on important projects focusing on our data and machine learning infrastructure. This includes:

  • Using open source machine learning tools to carry out research projects

  • Assisting in the build out of the team’s ML and distributed systems capability

  • Gaining a deep understanding of our business context and by leveraging your programming knowledge, propose improvements.

This role will be eligible for conversion to full-time status following successful completion of the internship. This is an exciting opportunity to both learn and have a significant impact on the team’s growth.


  • Completing or finished a PhD or Masters in a technical field such as Computer Science, Engineering, Mathematics, Physics or Similar

  • Solid programming skills in Python

  • Ideally you will have a strong interest or experience in Tensorflow, Dask or Spark

  • You have a keen interest in the financial markets and the drive and desire to work in a fast-paced, team-oriented environment

  • Pragmatic approach to ensuring delivery on a timely basis

  • You will possess practical problem solving skills with a great attention to detail

  • You are able to communicate effectively in both written and verbal English


Off-Cycle Internship

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