The Quantitative Technology Machine Learning Research Summer Internship is an intensive 10-week program that provides Summer Associates the opportunity to work alongside Full-Time Finance professionals and ML specialists on impactful applied research projects. Summer Associates will work within Morgan Stanley's Machine Learning Research Team for the entirety of the program. This is a highly motivated and collaborative team of scientists, technologists, and market practitioners. This team is responsible for working with business units and technology teams across the entire Firm to solve mission-critical high impact problems. The multi-faceted program features senior quant teach-in sessions, divisional speaker series, networking events, and community service. With individual coaching and continuous feedback, the program enables Summer Associates to experience and understand what a long-term career in ML Research within the Firm entails. Training Program:
The Summer kicks off with a week-long introductory training program, which provides an institutional contextualization to the work that Summer Associates will be doing through market-knowledge training, finance workshops, coding and product training. Following the training week, Summer Associates will continue to receive more individualized on-the-job training as they begin their daily work and projects. Summer Associates will have a direct manager, as well as a program mentor, both of whom will act as invaluable resources throughout their time at Morgan Stanley. Role and Responsibilities:
Qualifications and Skills:
- Independently tackling previously-unsolved research problems that have commercial applications.
- Machine Learning and other advanced quantitative methods in every line of business; the purpose of the central ML Research team is to create custom algorithms and tailored solutions.
- Leverage the technical expertise and research acumen you have been cultivating in your academic careers, and apply it to real-world financial and operating problems. (Successful candidates will have experience in conducting creative, hands-on, high-impact quantitative research.)
- Broad experience across multiple fields is a plus.
- Track record of publishing in competitive venues is highly sought after.
- You are pursuing a PhD degree in Computer Science, Mathematics, Physics, Statistics, Chemistry, Financial Engineering, Financial Math, Engineering, Quantitative Finance, or other related quantitative field.
- You have a deep understanding of statistical learning methods and strong mathematical academic training.
- You have excellent programming skills in Python or R, (C, C++, Java, etc. is a plus).
- You have a keen interest in financial markets.
- You have the drive and desire to work in an intense team-oriented environment.
- You have strong communication and organizational skills.