SPOTLIGHT on Eric Weber
This time around, we focus our SPOTLIGHT interview onto LinkedIn Data Scientist & Influencer Eric Weber.
Eric is a Senior Data Scientist at LinkedIn and also has a huge following of engaged Data Scientists through his thought provoking and insightful posts. If you're not following him on LinkedIn then you're really missing out! Enjoy the interview...
You're a Senior Data Scientist at LinkedIn, what does your role comprise of?
Data scientist encompasses a wide variety of roles at LinkedIn. Our foci include sales, marketing, product, data mining and machine learning, among others. I focus specifically on sales intelligence for our LinkedIn Learning product. We officially launched this part of the business in July 2016 and are growing quickly. Sales intelligence means we try to help our sales team be as efficient as possible in reaching the right customers. This involves a wide variety of tools and data using rule based and machine learning based systems. As a senior data scientist, I also am expected to take leadership roles in specific projects.
In this industry it can sometimes be hard to put into words, what it is that we do! When someone asks "So Eric, what do you do?" at a Christmas party later this month - how will you respond?
I wouldn’t want to ruin someone’s Christmas party with a 10 minute explanation so it would sound something like this: “My job is to understand and learn a business inside and out in addition to learning the data related to that business inside and out so I can deliver maximum value to the business using that data.” Of course, there is a lot more to it, but at the end of the day, this description touches on two major responsibilities of the job: know the data and know the business. This way people won’t avoid me at future Christmas parties.
Data Scientist's come in all shapes and sizes these days, what was your education and career path to get you to where you are today?
The variety in the field is one of my favorite things about data science! I “grew up”, so to speak, in teaching and learning and have always been passionate about those subjects. Teaching has always been my passion. Coupled with my interests and master’s in business analytics, my background in teaching mathematics and statistics makes me a teacher with an extreme quantitative interest. The reason I came to LinkedIn was the opportunity to affect teaching and learning at scale. I firmly believe that LinkedIn Learning has the chance to change the game in how we think about education for professional and personal development. Here, I have the chance to touch on all my interest areas: teaching, learning, statistics, mathematics and data science. It is pretty great!
You're very active on LinkedIn, posing insightful thoughts and questions on a daily basis to over 15k followers. How did you get into this, and how do you come up with your posts?
I remember the first post I made on LinkedIn about two weeks into working here. I wrote it on the train to San Francisco in about three minutes. Since then, my posts have taken me on quite a journey. I still today think it is crazy that people follow me for advice and insight but I do my best to be reflective about what I learn and try to communicate that in a succinct way to others interested in data science. All of my posts are driven by experience. I do not plan them ahead of time. I usually spend 5-10 minutes writing each one and try to write from the heart. I don’t really have a set of topics in mind. Instead, the topics reflect my day to day experience. I’m extremely grateful for the interactions I have and the followers I’ve gained along the way. It is pretty humbling!
Through interacting with your followers, what is a common theme you're starting to see, or common question you're being asking within the Data Science field?
Given the LinkedIn platform, it is not surprising the most common question I get is “how do I get into data science?”. The most common theme I see is actually disagreement about how to define the field and where the field is going. I love the disagreement. It shows the incredible diversity of thought and experience that data scientists bring to the table. I think the differences we have make the field better and set us up for longer term success.
Who are your Data Science inspirations - what is it that makes them so inspiring?
My data science inspirations come from many sources. First and foremost, the people around me. I’m fortunate to be on an incredible team at LinkedIn and am amazed every day at the intelligence, thoughtfulness and skill of the data scientists around me. I have mentioned my teammates in a number of posts but my day to day inspiration for this field comes from them. Second, I have found so many amazing connections on LinkedIn that push me to think critically. Some that come to mind immediately are Vin Vashishta, and Beau Walker, two great examples of thoughtful data scientists who help and inspire others in the field. Third, I would be remiss not to mention the amazing professors and cohort members I had at the University of Minnesota – Carlson School of Management. The business analytics program there changed the course of my career. Together, all of these people and places have inspired and continue to inspire me daily.
Within either your role at LinkedIn, or your social media activity - what is something that you really struggle with or perhaps don't find natural?
I find it difficult to “sell” myself. People talk a lot about building a personal brand or reputation and have an easy time promoting themselves. I don’t find that to be easy and actively try to avoid it sometimes. However, there are situations in which it is important and I’ve learned how to package what I do for various audiences. However, I don’t think that part will ever come naturally to me!
What technique/infrastructure/tool are you currently itching to learn more about?
This one is pretty easy: deep learning. It is revolutionizing a lot of areas of data science and as it hits mainstream, will redefine both the problems we can hope to study and the answers which we can hope to get from asking questions about those problems. I’m putting a good deal of time into learning about these advances and changes in the data science space but continually find new things and topics I need to explore. There is so much and it is changing so fast. I think it will be difficult to really satisfy the itch to learn more about deep learning as it explodes in popularity.
What do you see as the 'next big thing' in Machine Learning & AI?
To me, the next big thing as far as techniques go are in AI and deep learning. A lot has been said about these topics and I don’t pretend to be an expert in them. I just know they are reshaping the data science world and will continue to do so. Another ‘big thing’ is data protection for consumers. With GDPR being implemented in May, I see a lot of big changes on the horizon for how we use and think about user’s personal data. GDPR will certainly affect a lot of marketing and sales intelligence and will change our ability to personalize things for consumers. The lack of discussion around this topic has actually surprised me given the scope of changes it brings.
What are your passions & hobbies outside of Data Science?
I love being outside. I’m fortunate to have tons of hiking and cycling areas in Northern California. I also really enjoy traveling. I think travel provides a healthy perspective on the world. Lastly, I keep up with the news. This has been particularly exhausting in the last year with the rapid news cycle worldwide, but I find it important to understand the political and economic forces that shape our day to day lives.