NISS Industry Career Fair 2024

Join us at the NISS Industry Career Fair 2024, where professionals from Google and Salesforce will share their insights and experiences in the ever-evolving tech landscape. This event features three speakers from industries who will share their career journeys, provide insights into what it's like to work at their company, and discuss the impactful roles statisticians and data scientists play in driving innovation. Learn about the challenges and rewards of working in one of the world’s leading tech companies, and gain a deeper understanding of the current and emerging opportunities in the field. Whether you're considering a career at these companies or simply curious about the industry, this event offers a rare chance to hear from professionals at the forefront of data science and statistics. Our moderator will guide the Q&A session, ensuring a dynamic and engaging discussion. Don’t miss this chance to discover what it’s like to work at Google and Salesforce and explore the exciting opportunities available for statisticians and data scientists at the NISS Industry Career Fair 2024!

 

 

Speakers:

Yichen Zhou, Google Research

Seth Strimas-Mackey, Google Ads

Sarah Tan, Responsible AI at Salesforce

 

Moderator:

Grace Deng, Google

 

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Agenda:

During this career fair, each presenter will have 15 minutes to address the following general topics:

1. Describe your career journey, and what your current position entails, 
2. What are the job opportunities for statisticians/data scientists/analysts in your company?
3. Describe the range of skills statisticians/data scientists/analysts need to succeed in your company?
4. What is the career path for statisticians/data scientists/analysts in your company?
5. What advice would you give to students based on your experience?

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About the Speakers: 

Sarah Tan Sarah Tan is a Director in Responsible AI at Salesforce. Sarah works on problems on AI safety for Salesforce built generative models throughout the entire model development and deployment lifecycle. Sarah also holds a Visiting Scientist appointment at Cornell University and is president of the Women in Machine Learning (WiML) nonprofit. Sarah received her PhD from Cornell University. Profile: Sarah Tan (shftan.github.io)

 

 

Seth Strimas-Mackey is a data scientist at Google, known for his expertise in statistics and machine learning. He completed his Ph.D. in Statistics from Cornell University in 2022, where he worked on high-dimensional data analysis and latent factor regression. His research has been published in several prestigious journals, and he has collaborated with notable scholars like Florentina Bunea and Marten Wegkamp. At Google, Seth applies his deep knowledge of statistical methods and machine learning algorithms to solve complex problems, contributing to advancements in data science and technology. His work continues to impact both academic and industry circles, making him a respected figure in the field. Seth completed his PhD in Statistics at Cornell University and has a strong math background and experience working as an NLP researcher in tech. Seth is passionate about natural language understanding and AI, including language models, topic modelling, and latent variable models.

Yichen Zhou is a researcher at Google, focusing on machine intelligence and data mining. Recently, Zhou and colleagues designed a time-series foundation model for forecasting inspired by large language models used in Natural Language Processing (NLP). Their model achieves impressive zero-shot performance on various public datasets, approaching the accuracy of state-of-the-art supervised forecasting models for each individual dataset. If you're interested, you can find more details about their work in their research paper titled "A decoder-only foundation model for time-series forecasting" here: https://arxiv.org/abs/2310.10688

 

About the Moderator: 

Grace Deng is currently a research data scientist at Google and formerly interned at Amazon Search and Instagram. She is a member of the NISS Affiliates Leadership Committee, and sits on the Industry Affiliates Subcommittee. Grace completed her PhD in Statistics from Cornell University in 2022 and received her undergraduate degree at UC Berkeley in Statistics and Economics. Her research focus includes generative ML/AI models for synthetic data and Bayesian time series models. She is the recipient of the Cornell Hemmeter Entrepreneurship Award (2020) and JSM Best Student Paper Award at JSM (2021), as well as various hackathon and datathon awards.