Nina Zumel

Nina Zumel

Data Scientist with Win-Vector, LLC and Wallaroo.ai

Curriculum Vita

Education

Carnegie Mellon University, The Robotics Institute
Ph. D. Robotics, 1997
University of California, Berkeley
BSEE, Dept. of Electrical Engineering and Computer Science, 1989.

Professional Experience

Wallaroo 
VP, Data Science Practice, 2021-Present

The Wallaroo platform makes it simple, fast, and very low cost to get AI and ML algorithms live against production data. My role is to advocate for the needs and interests of data scientists, to insure that Wallaroo is a productive and pleasant experience for them.

Win-Vector, LLC. San Francisco, California
Principal Consultant, 2008-Present

Win-Vector conducts data analysis and statistical research for a variety of private sector clients, particularly in the biotech, finance, and internet sectors. Engagements that I have led or been involved in have included: revenue attribution for Google ad-words, customer modeling from online transaction data, product recommendation systems, and loan risk modeling.

I am the co-author of the popular Practical Data Science with R, an introductory data science text written from a practitioner's perspective.

I have developed original content for a number of training courses including:

Contributor to CRAN R projects:

Occasional contributor to the Win-Vector blog; started the popular Statistics to English Translation series.

Quimba Software, Inc. San Francisco, California
Co-founder, Owner. 2001-2008

Quimba Software provided research services and specialty software development to clients in Government and Industry. Our application areas included Intelligence, Homeland Security, and Emergency Response/Emergency Management.

Optivo, Palo Alto, California
Staff Software Engineer, Numerics, 2000-2001

Developed the core numerics engine for Optivo's Live Price Testing and Price Optimization product. The Optivo Price Manager monitors market demand for products on online channels in real or semi-real time, and adjusts prices accordingly to optimize the merchant's business objectives. A trial version of this system was successfully deployed for several online merchants.

Designed the testing and optimization algorithms; the testing algorithms balance issues of statistical significance and test-cost, as well as compensating for non-stationarity in the pricing environment. Wrote the Java-based optimization module, and wrote or worked on several SQL and PL/SQL scripts and procedures in support of the optimization module. Worked closely with the User Interface team in designing the online Analytics reports which a merchandiser would use to monitor the performance of his or her products while using our system. Worked with the Product Management and UI teams on issues of the accessibility and ease of use of our system by non-technical customers, such as merchandisers.

Artificial Intelligence Center, SRI International, Menlo Park, California
Computer Scientist , 1997 through 2000

Computer Scientist at the Artificial Intelligence Center. Worked on several projects in the areas of Knowledge Representation, Inference and Reasoning.

Published Books

Zumel, Nina and John Mount. Practical Data Science with R, 2nd Edition. Manning Publications, 2019

Selected Papers

Mount, John and Nina Zumel. The vtreat R package: a statistically sound data processor for predictive modeling. The Journal of Open Source Software, 3(23), 584. (2018)

Franco, Zeno E, Nina Zumel, Kathy Blau, Knute Ayhens-Johnson, and Larry Beutler. Causality, covariates and consensus in ISCRAM research: towards a more robust study design in a transdisciplinary community. International Journal of Emergency Management, Vol. 5, pp 100-122. (2008)

Patents

Vladimir Gorelik, Andrew Atherton, and Nina Zumel. Method and apparatus for automatic pricing in electronic commerce. United States 7970713