We are looking for a creative and enthusiastic Machine Learning Engineering Intern to join our Sector Data team to build the best data and quantamental platform for our investment teams on the street. We’re responsible for constructing models to predict key performance indicators of publicly traded companies, evaluating datasets for predictive power and efficacy, building engineering infrastructure to support the myriad of analytics, and pushing the envelope on quantamental research. Our singular goal is to help our investment teams use data to make better investment decisions. Our ideal candidate will be someone deeply passionate about data and modeling with a strong attention to detail and a can-do, team oriented, collaborative attitude. We are also most interested in candidates who want to and can make an impact. In this role, you will be exposed to the following:
- Responsibilities range from (i) leveraging traditional and/or alternative data to create or improve predictive models to (ii) developing statistical and machine-learning models to enhance the research and development system.
- Opportunity to create algorithms to monetize the predictive signals.
- You will be part of a research environment, and will write code, apply statistical analysis / use the latest machine learning and deep learning tools to run experiments and enable data-driven decision making.
- You will be expected to set up large-scale tests and deploy protypes of promising ideas quickly, managing deadlines and deliverables while applying the latest theories to develop new processes.
- Opportunity to work directly with Analysts, Quants, and Portfolio Managers to generate new investment insights.
WHAT YOU’LL BRING
- Advanced Master’s or PhD degree in Computer Science, Statistics, Physics or related quantitative field.
- Work experience selecting, developing, refining and evaluating machine learning/deep learning models.
- Previous exposure to reinforcement learning and/or genetic algorithms is a strong asset.
- Demonstrated experience applying statistical methods (classification, regression, clustering, etc.) with large datasets.
- Excellent programming skills in Python or a similar language and experience in ML libraries, including Tensorflow or Pytorch.
- Previous work experience with Spark and SQL.
- Experience with distributed systems and cloud environments (knowledge of key AWS services).
- Prior experience in finance is *not* required.
- Participation in open source community or recent side projects involving data analysis is an asset.
- Demonstrated experience highlighting creativity and thinking “outside the box”. Entrepreneurial spirit and a strong desire to innovate. Ability to thrive in an autonomous environment. Meticulous attention to detail.