Speaker: Shanshan Qiu, JPMorgan Chase

Time: Wednesday, February 18, from 2:30 pm to 3:30 pm

Location for in-person participants: 1146 FAB 

Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1

Title: From Mathematical Foundations to Real-World Impact: Data Science Across Industries

Abstract: Modern data science is built on a deep foundation of mathematics, statistics, and operations research, even as tools, technologies, and application domains continue to evolve rapidly. In this talk, I will share how strong analytical fundamentals have enabled me to transition across multiple industries—including manufacturing, supply chains, consulting, and financial services—while consistently solving large-scale, high-impact decision problems.

Drawing from real-world industry examples, I will discuss how core concepts such as statistical modeling, optimization, probabilistic reasoning, and algorithmic thinking underpin applications ranging from forecasting and segmentation to resource allocation and network optimization. Beyond the models themselves, I will highlight practical challenges in translating theory into production systems, collaborating across disciplines, and ensuring interpretability and trust in data-driven decisions.

In addition to technical insights, I will also share career lessons learned beyond technical skills, including how to communicate analytical results effectively, work with diverse stakeholders, navigate career transitions across domains, and build long-term impact as a data scientist.

The talk is intended to be broadly accessible and will emphasize intuition, modeling choices, and practical advice, with the goal of demonstrating how rigorous mathematical thinking and complementary professional skills together enable sustained success in data science careers.

Bio: Shanshan Qiu is an Executive Director of Data Science at JPMorgan Chase, where she leads large-scale analytics and optimization initiatives supporting network planning and enterprise decision-making. Prior to joining JPMorgan Chase, she spent over eight years at Ford Motor Company and three and a half years at Deloitte, leading data science teams across manufacturing, supply chain, finance, and enterprise analytics. She holds a Ph.D. in Industrial and Systems Engineering, with research focused on applying machine learning and optimization to complex systems. Her work bridges theory and practice, with a strong emphasis on translating mathematical and statistical models into scalable, real-world impact.

______________________________________________________________________

CAD Seminar Series: Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)

The CAD Seminar Series is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.

Objectives:

• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.

• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.

• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.

Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.

Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.