We offer a unique industry leading development program that combines academic rigor with hands-on industry experience.


Christine Pfeil, Program Director

The Data Science Development Program (DSDP) trains promising, recent graduates to become well-rounded data scientists for MassMutual. The DSDP is an intense, three-year program that combines academic coursework with the practical experience of working on data science projects for MassMutual. At the end of three years, this would be equivalent to earning a Master's degree in data science with deep practical work experience. By the end of three years, members should be capable of making independent and material contributions to data science services.

Program Highlights

Academic Rigor

Each member of the program spends part of their time completing a blended curriculum of Five College coursework, in-house workshops led by local faculty, and online courses (e.g., via Coursera). The coursework is customized to address individual weaknesses/interests and guided by advisors. Typical coursework encompass upper-level computer science classes, such as machine learning, graphical models, natural language processing, and databases, and upper-level statistics classes, such as mathematical statistics, applied regression, multivariate analysis, and applied experimental design.

Practical Industry Experience

Equally as valuable as formal academic training, members of the DSDP are fully immersed in high-impact data science projects for MassMutual under the direction of a senior data scientist. We provide every member the opportunity to learn new methods and gain exposure to different facets of a large organization, by contributing to at least two projects each year. Data science projects typically involve statistical data analysis, predictive analytics and machine learning as well as developing web-based interactive visualization tools.

Challenging and Fun!

Our work environment has a collaborative, start-up culture and serves as a joint classroom and workplace for DSDP members and senior data scientists. We hold weekly lab meetings to present technical ideas, new models, or interesting code libraries. We also sponsor biannual internal data challenges with friendly competition and prizes, similar to the growing trend of datafests and hackathons.


Class of 2018

Hayley Carlotto

John Karlen

Freddie Sanchez

Yue Tang

Grace Yoo

Class of 2019

Em Beauchamp

Lizzie Kumar

Jordan Menter

Martha Miller

Edward Pantridge

Karla Villalta

Class of 2020

Cheng-Yin Eng

Matthew Girard

Tom Jeon

Madison Laethem

Jeanie Lim

Isha Raut

Tam Tran The