The Data Science Education Podcast · Berkeley Data Science

Data Science Graduates in Industry (feat. Samantha Chean-Udell, Elda Pere, and Carlos Ortiz)

March 24, 2023·25 min·2 clips

Eric and Kalechi from UC Berkeley kick off a discussion on how data science graduates apply their skills in real-world roles.

This episode of the UC Berkeley Data Science Education Podcast features a conversation with recent graduate Samantha "Sammy" Rauscher, hosted by Eric Van Dusen and Kalechi Nebedam from Berkeley's Data Science Undergraduate Studies. The discussion centers on the practical application of academic data science training in an industry role. Sammy now works as a digital product manager at Converse, focusing on e-commerce features. Her primary responsibility involves managing web analytics through a platform like Adobe Analytics to optimize the online shopping experience. Sammy notes her university coursework did not specifically cover such industry-standard analytics tools, contrasting them with more classic academic projects like spam filters. A key part of her job is auditing and managing the numerous tracking tags on the company's website to ensure accurate data collection. She explains this data is then used to analyze user behavior, such as navigation paths and feature interactions, to inform product decisions. One surprising insight is her assertion that the most critical skill from her education was not a specific technical tool, but learning how to ask precise, actionable questions of data. She emphasizes that product management requires translating business goals into clear analytical frameworks for data scientists and engineers. Sammy describes a common industry challenge where data collected from website tags can be messy or improperly implemented, leading to flawed analysis. A memorable moment is her candid admission that she initially felt unprepared for the specific software used in her role, despite her strong foundational training. The conversation reveals that effective communication and cross-functional collaboration are often more vital than advanced statistical modeling in her day-to-day work. She suggests academic programs could better prepare students by incorporating more case studies on data quality issues and stakeholder management. The tone is informal, conversational, and reflective, focusing on practical advice and personal experience. The style is educational and interview-based, with hosts prompting for specific details about the transition from academia to industry. This episode would greatly interest current data science students and early-career professionals curious about product management roles. Listeners seeking deep technical tutorials or theoretical debates might find the content less aligned with their interests.

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