We offer a unique industry leading development program that combines high-impact industry experience with hands-on training, mentorship, and coursework.
Christine Pfeil, Program Director
The Data Science Development Program (DSDP) trains promising, recent graduates to become well-rounded data scientists at MassMutual over three years. Participation in the development program includes mentorship from senior data scientists, exposure to real world, high-impact data science projects, in-house training and workshops, and tuition sponsorship for graduate level classes at UMass Amherst. By the end of three years, junior data scientists should be capable of making independent and material contributions to data science efforts.
Junior Data Scientists in the DSDP are fully immersed in high-impact data science projects for MassMutual under the direction of a senior data scientist. Junior data scientists work together with MassMutual’s data scientists to solve challenging problems in fields such as finance, operations, marketing, digital initiatives, and product development. We provide every member the opportunity to learn new methods and gain exposure to different facets of a large organization. Data science projects typically involve statistical data analysis, predictive analytics, modeling, and machine learning as well as developing web-based interactive visualization tools.
The MassMutual Data Labs in downtown Amherst consists of a dynamic team that is actively engaged in dozens of high-impact projects across the enterprise, frequently working remotely with teams based in Springfield, MA, New York, and Boston. Our environment and workspace reflects our dedication to innovation and learning, and has a collaborative, start-up culture. We hold weekly training meetings to present technical ideas, new models, or interesting code libraries. We also sponsor biannual internal data challenges with friendly competition and prizes.
Each member of the program completes a blended curriculum of Five College coursework, in-house workshops led by local faculty, online courses, and graduate-level study. The coursework is customized to address individual 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.