Conversations with Sam Bauer, Data Scientist & Bain Extern
March 13, 2018
Our team is a tight-knit group passionate about product, machine learning and data science. Sam joined the team at MakerSights as part of his 6 month Bain externship, and we wanted to share his experiences from both a startup and consulting lens.
Tell us about the program at Bain that brought you to MakerSights
I joined MakerSights on what is known at Bain as an externship; Bain offers a unique opportunity in your third year to take six months away from your home office, and one of the options is an externship at a different company. The idea is that, as a consultant, it is invaluable experience to work at a company and get internal, hands-on experience and give context to the challenges that companies face when implementing process and change throughout their organization.
What did you study in college, and how did you end up at Bain?
I studied mechanical engineering at Dartmouth, which has a strong focus on project based work and liberal arts. I realized that a broad problem solving ability translated well to the business world, and enjoyed working in more high-level, high interaction areas after finding that engineering can be quite siloed. I liked the multi-faceted questions that you get to answer in strategic roles, which led me to my internship and full-time role at Bain.
How did you choose MakerSights?
I had a connection to MakerSights through a friend, who had previously spent his Bain externship at Makersights as well. During his time, MakerSights was only a three person team, working out of a WeWork space, but he spoke incredibly highly of his time and experience there. I was extremely interested in machine learning, and I also knew I wanted to spend my internship in a small, tech focused company. I’ve always worked in a clear chain of command, so the externship was an opportunity for me to work in a flatter organization, and get exposure to different parts of a business and see how nimbly small teams work and execute goals. I had experience in retail from working in a the retail practice at Bain, but as an engineer by training, I also wanted to work in a technical environment and be able to get involved in the design process. It was clear MakerSights was an exciting team with strong leadership, and I knew that it would be a natural fit for my skills and interests.
What projects have you spent your time on at MakerSights?
I’ve been working on the data science side, figuring out how we should be thinking about reaching customers to test product in the future. We’re currently testing with externally recruited audiences, enabling brands to gain insights outside their existing customer base, which has provided an exciting opportunity for some of our key brand partners.
What have been your key learnings in your time working at a startup?
What I’ve found most interesting is the difference in where companies choose to focus their time. In consulting, 90% of total time is spent fully exploring every possible option to ensure we are as informed as possible before making a formal recommendation. It’s a very thorough process. At MakerSights, we subscribe to the “fail fast, fail often” approach; we spend about 20% of our time hashing out ideas, and then 80% of the time testing, tuning and productizing. It’s a super effective approach to different problems, and it’s cool to see it in practice. We prioritize embracing feedback and being very customer focused, and many of our features are driven by that.
How did your experience at Bain prepare you for MakerSights?
One of the best things about Bain is its team-oriented culture. It is a really fast paced environment, and you learn how to ramp up and add value on a new topic very quickly, alongside working efficiently with new people. Bain is great at teaching process for approaching vague strategic questions holistically, breaking it down to smaller chunks that you can tackle and answer, and then building it back up to a greater strategy.
What advice do you have for brands in 2018?
Big data and buzzword trends have been impacting industries across the board, and now it’s catching up with retail. In today’s world, it’s easy for brands to capture mass amounts of data but not as easy to capture it in an actionable way. What is most important for brands is setting themselves up to obtain quality data and ask themselves how they should be thinking about it. What’s a representative metric of how we decide what’s a good product? How do we decide which stores are our best stores? How do we think about selling? By making sure the data captured adds value, brands can scale to larger learnings and extract from there; if you don’t start with a framework for quality data, you’re never going to have meaningful information to make quality decisions.
Tell us a fun fact about yourself!
I’ve always been a data-oriented person, and as a kid that manifested in an obsession with weather. I’d watch the weather channel all day, look at updated national weather reports, and measure the snowfall outside. My dream job was to become a hurricane chaser like the ones you saw on television, reporting from the storm. I still have a weird interest in weather, and still track storms. I recently took an avalanche safety course and really geeked out learning about the snow science.
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