Feb 24: Final Application Deadline for Spring Bootcamp! Apply Now

Meet Kevin Birnbaum, New Metis Sr. Data Scientist

By Emily Wilson • October 17, 2019

Kevin Birnbaum is a new Sr. Data Scientist on the Corporate Training team at Metis. Previously, he was the first data scientist at Munchkin, a global baby products company. In that role, Kevin incorporated machine learning, statistical testing, and data visualization into marketing and demand planning. Kevin established the first Data and Analytics team at Munchkin, where he continued to evolve the company culture by integrating data science into everyday decision making. Kevin earned a BA in Applied Mathematics and an MS in Information and Data Science from UC Berkeley. We recently caught up with him to ask a few questions about his career thus far, his new role at Metis, what interests him most about data science, and more.

You come to Metis after nearly 4 years at Munchkin, where you were the company’s first data scientist. What were the pros and cons of being the first data scientist at a company of that size and scale?

I found that the pros and cons were almost always two sides of the same coin:

  • - There was a lot of opportunity for improvement and growth using data science tools, but also it consistently felt a bit behind data science industry standard.
  • - I had the freedom to decide my own projects and tackle subjects that interested me personally, but I did not have a technical team or mentor with whom to collaborate and bounce around ideas.
  • - I had the opportunity to build tools from scratch from start to finish. On the other hand, I did not have any strong prior reference points to build off of.

 Within your new role at Metis, you train corporate teams in data science. Why do you believe companies should skill-up their existing teams? What value can it bring?

This goes well with the first question. Being the first data scientist at my company, I was able to see the direct return on investment (in this case, I was the investment) of introducing a data science skillset to a company or team. Taking the organization  from having very little data science skills to having it as a core component of the team helped in the following ways:

  • - We were able to democratize the data and make information readily available for cross-functional decision making.
  • - We used machine learning in order to forecast future sales and save the company millions of dollars and hours of work.
  • - I was able to share my knowledge and understanding to spread data literacy to enable different teams to leverage the data made available for making decisions. 

What interests you most about data science at this point in time?

It’s tough to say. Given that data science covers such a wide range of topics, I sometimes feel like a kid in a candy shop. Currently, I see myself diving into two topics. 

The first is time series forecasting. The major m4 forecasting competition just happened in December 2018 (first m-competition since 2000) and it was interesting to see the new methods that are now winning these competitions. There are a ton of exciting topics within time series to dive into, and so many new developments with deep learning/combination models, etc. Very fascinating.

The second is working with Python in Spark. The PySpark API has improved the process of analyzing data at scale.

What are some of your favorite data science blogs, books, etc. that you think others might benefit from checking out? 

A book I read when I first started – and still reference – is Hands-on Machine Learning with Sci-kit Learn and Tensorflow by Geron Aurelien. She is actually about to release a second edition that includes a large section on Keras, which I am excited for.

Another book that I consider an easy handbook – which isn’t as much about data science, but could be useful for non-data science folks – is How to Lie with Statistics by Darrell Huff.  

Also: 

How do you like spending your free time?

I enjoy being active, whether that means hitting a new local exercise class, playing soccer or basketball with friends, or even just going out for a run. I just moved to New York City, so right now I am spending a ton of my free time (and money) exploring all NYC has to offer!

_____

Learn more about Metis Corporate Training, which enables businesses to capitalize on the talent already working under their roofs through on-site training on topics like Data Literacy, Machine Learning, Data Engineering, and much more.


Similar Posts

business resource
Does the Data Speak for Itself?

By Alex Nathan • November 25, 2019

In this post, Alex Nathan, Co-Founder of Aventrix Analytics and part-time Metis Corporate Training Instructor, asks the question: Does the Data Speak for Itself? As it turns out, the answer is no, because data alone is typically not enough to arrive at any conclusions. The following equation explains the source of discrepancy: DATA + ASSUMPTIONS = CONCLUSIONS. Read on to find out how data scientists can address this issue.

business resource
Demystifying Data Science Talk Recap: Natalie Evans Harris on The Ethics of Data Governance

By Emily Wilson • February 06, 2020

This is the 6th installment in an ongoing blog series to recap the talks given on Day 2 of our 2019 Demystifying Data Science live online conference. In her talk, Natalie Evans Harris discusses the ethics and opportunities of data governance.

business resource
Metis Makes 2019 Training Industry's Top Training Companies List

By Metis • December 09, 2019

Training Industry Inc., the leading research and information resource for corporate learning leaders, announced the selections for its 2019 Top Training Companies™ lists, focused on the IT training sector of the learning and development market. We're proud to announce that we're included on its 2019 IT Training Watch List!