The Columbia Threadneedle Investments 2021 Summer Undergraduate Intern Program is designed to provide students with first-hand investment management experience. The Quantitative Research Team at Columbia Threadneedle Investments is seeking a talented individual to join their team as a research intern for the summer of 2021. The successful intern will use rigorous analysis, creativity and technical skills to generate insights that directly contribute to the team’s advancement. In this role, by combining theory, practice and advanced technology, the candidate is expected to work with other team members on multiple impactful projects involving alpha signal discovery, analytics development and strategy implementation.
This position is an excellent opportunity to gain insight into the field of quantitative investing from an experienced team of investment professionals as well as exposure to all aspects of the investment process. In addition to specific on-the-job learning, the program will also be supplemented with several networking opportunities and broad asset management overview sessions. Additionally, all interns will prepare a presentation based on their specific experience. Upon successful completion of the internship, full-time employment offers for the Columbia Threadneedle Investments Leadership Development Program may be extended.
• The intern will work on impactful research & development projects
• Pre-process large data sets for signal processing and model estimation, including validation, cleaning, normalization, dimension reduction and visualization
• Evaluate innovative techniques for delineating noise from tradeable signals and constructing models from many diverse indicators
• Assist senior researchers with the maintenance and enhancement of existing stock selection models
• Present research findings and recommendations to the firm at the end of the internship
• Junior status pursuing bachelor’s degree in accounting, finance, economics, applied mathematics, statistics, computer science, or related field.
• Minimum 3.0 GPA
• Strong analytical abilities in the form of familiarity with statistical inference, machine learning, optimization theory, and some exposure to finance
• Proficiency in scripting, preferably R and Python
• Exposure to database management tools
• Demonstrated ability to complete projects in a constrained period
• Demonstrated interest in financial markets and systematic investing
• Clear, concise, proactive communication skills
• High level of self-motivation and ability to multi-task
• Work well in a group setting