Senior executive with over 20 years of building, leading, and advising world-class machine learning, AI, and data science teams at companies at stages from startup to Fortune 50.
In his work as a data scientist and strategy specialist, Ed leverages rich experience in business strategy as well as superior quantitative/analytical and leadership/interpersonal skills. Across his professional experiences, he's established a proven record of generating and implementing creative ideas to bring about measurable change.
An active member of the AI and data science communities, Ed regularly presents at national and international conferences and workshops and has been named a "Top Big Data Pro To Follow On Twitter".
As Vice President of Machine Learning at Nurx, Ed is building and leading a new team of machine learning engineers and mathematicians who push the envelope of AI technologies (e.g., machine learning; deep learning; Hadoop; Spark) to help Nurx reinvent healthcare delivery.
Vice President of Data and Machine Learning
May 2019 – Present
I lead Data and Machine Learning for Nurx, a $100M+ digital health startup (YC W16) backed by Kleiner Perkins and Union Square Ventures. I am charged with developing Nurx's data and machine learning strategy and vision and putting in place the organizational structure necessary to execute on that strategy. My day-to-day tasks include a balance of collaborating with Nurx's other senior executives, recruiting and developing top machine learning and data talent, and translating our business opportunities and objectives into solutions based in data and machine learning applications.
CIO & Chief Data Scientist | Innovation,
Data Science, & Artificial Intelligence
Mar 2016 – May 2019
At PenLink I built and led a newly-formed team of engineers and mathematicians who push the envelope of AI, data science, and visualization technologies (e.g., machine learning; deep learning; Spark; Hadoop) to support PenLink's best-in-class software offerings.
Data Science Leaders Network
Workshop Leader: Designing and Building a Data Science Team
Creating and growing a successful team in the current competitive hiring market can be a challenge. Learn valuable hiring strategies from creating the perfect job description right through creating career pathways to keep your data scientists engaged. This session also explores best practices for evaluating talent and offer interesting interviews techniques when under time constraints. You will equally have the opportunity to compare different team models to establish the best fit for your company.
4x24 Executive Leadership Program - Big Data/Analytics
The 4x24 Big Data/Analytics group engages executives-of-consequence in a thoughtful dialogue on the direction and impact of federal government policies and procedures. Additionally , we address the role of the private sector in shaping these actions.
Sears Holdings Corporation
Chief Data Scientist; DVP, Pricing | Data Science & Artificial Intelligence
2015 - 2016
As Chief Data Scientist I built and led a newly-formed team focused on developing production-scale solutions to identify and capture $100M+ margin enhancing opportunities in the areas of pricing, offers and other appropriate sales and marketing instruments. We used methods of modern day artificial intelligence – e.g., machine learning, Bayesian simulation (MCMC), in-memory/distributed computing (Spark; Hadoop) – to overcome the limitations of quantitative approaches traditionally employed in pricing and marketing. As leader of this group I was charged with not only working with Sears' senior executive team (including CEO) to communicate our vision and strategy, but also identifying, recruiting, and retaining the data science talent critical to supporting this major initiative.
Senior Engineering Manager - Big Data Analytics & Data Science
2013 - 2015
As the leader of Seagate's Big Data Analytics initiative, I built and led a team of highly trained data scientists, engineers, and researchers to push the envelope of Big Data Analytics (e.g., machine learning; deep learning), primarily related to next-generation Cloud technologies (Hadoop; Spark). In this role I served as strategist, subject matter expert and external spokesperson for this newly-formed initiative within Seagate’s Data Center Operations group.
University of Colorado at Boulder
Chair and Assistant Professor: Quantitative Methods
(Research & Evaluation Methodology Program)
Aug 2004 – Aug 2011
Teaching and research in basic and advanced statistical methods. Published 75+ articles & presentations. Chaired two and served on four national professional committees. Advised five state departments of education. Regularly taught five courses:
McKinsey & Company
Senior Associate (Analytics, Pricing, & Portfolio/Risk Strategy Expert)
San Francisco, CA
Aug 2001 – Dec 2004
Client engagements primarily in (1) institutional investment; (2) professional services; and (3) public sector: (1) Served North American and European institutional investor clients (from C-level executives to individual portfolio managers) on issues of policy, organization/governance, and investment/risk strategy. (2) Served global professional services clients on issues of pricing, organization, and operations. (3) Served state (California), local (Bay Area), and public and private educational organizations on issues of strategy. Functional expertise includes pricing and strategy; risk-based pricing; alternative investment (private equity, venture capital, hedge funds, and natural resources); real options asset pricing models
PhD Psychological StudiesActivities and Societies: First student in my school to be awarded the 3-year Stanford President's Full-Tuition Graduate Fellowship in Science and Engineering. Received "Outstanding" teaching ratings in eight basic and advanced statistics courses. One of three Stanford students named to Stanford Trustees' Committees on Research and Graduate Studies. Invited speaker for Inaugural Celebration of Stanford President's Graduate Fellowship in Science/Engineering Dissertation: Bootstrap Strategies for Variance Component Estimation: Analytical and Empirical Results. Dissertation Committee: Richard Shavelson, Edward Haertel, Bradley Efron, Kenji Hakuta, and Ewart Thomas. Dissertation study established new algorithms to estimate variance components and standard errors. Study mathematically demonstrated bias in bootstrap estimation of variance components, accuracy of which was demonstrated via Monte Carlo simulation. Study solved problem that had puzzled researchers for over 13 years. Dissertation won both national award (NCME's Outstanding Dissertation Award), as well as $20,000 Stanford fellowship. Taught/co-taught seven courses:
M.S. StatisticsActivities and Societies: Completed doctoral courses in applied statistics and statistical learning in the course of earning my M.S. under advisor Ingram Olkin.
MA Quantitative and Qualitative MethodsActivities and Societies: Awarded "High Honors" in passing Master's Comprehesive Exam. Thesis: Validity of intercollegiate athletic eligibility standards as predictors of student athlete collegiate success. Invited by Nebraska football coach Tom Osborne to prepare thesis findings for inclusion in Big XII deliberations regarding eligibility of 'partial qualifiers' .
B.A. Mathematics (Minors in English and Psychology)Activities and Societies: President of 135-member fraternity. Awarded Regents Scholarship; Eastman Honorary Mathematics Fellowship; and U.S. Department of Education's Robert C Byrd Fellowship. Named to 13-member senior honorary (Chancellor's Society of Innocents) for outstanding scholarship, leadership, and service. Elected 1993 University of Nebraska Homecoming King by student body of 25,000-student campus. Trained and managed two teams of 26 student orientation leaders.