by Jeremy Stanley, VP of Data Science at Instacart
Six months ago my wife and I finally realized a dream of ours – we bought a house in Brooklyn. When the last box was unpacked, I reveled in the thought that we might not move again for decades. Our daughters were in good schools, my wife’s small business was thriving, I loved my job, and our puppy had regular play dates with cute neighborhood dogs.
Then we decided to move everything – right down to the puppy crate – across the country so I could join Instacart as our VP Data Science. It’s a testament to how amazing the opportunity is (and how much my wife loves me!) that we uprooted our family so I could help scale and evolve an entirely new way of shopping for groceries.
Why did I join Instacart? To begin with, I love to cook. In college, I worked my way up from a dishwasher to a line cook in a vegetarian cafe, and for a year learned everything I could from my peers. But I abhor crowds (growing up in Kansas, they were few and far between), and while I enjoy picking out produce and discovering new products, I avoided shopping in the big city. Combined with a busy schedule, cooking became relegated to holidays and special occasions.
Since I began using Instacart, I have been able to return to cooking regularly, and our home is filled with fresh produce and healthy new ingredients. I look forward to shopping using the service, and the stress of fighting crowds at grocery stores has faded into a distant memory. Working hard to improve a product that I love to use is tremendously rewarding, and the challenge of creating a shopping experience that is even better than shopping in stores is an inspiring challenge!
Behind the scenes, many complex problems must be solved to provide customers with amazing shopping experiences on Instacart. How do you help customers explore hundreds of thousands of items available in their grocery stores online? How do you consistently deliver great customer experiences when thousands of personal shoppers are picking and delivering groceries in one or two hours? How do you optimize everything to ensure that prices remain low, shoppers are well compensated and orders are rarely late or incorrect? Data science is critical in solving all of these challenges, and smart data-driven products translate directly into increased growth and profitability.
Instacart has a very unique business model, and that is why data science is so important for our success. Instacart is in part an ecommerce marketplace, and so we have all of the same fascinating catalog, search, recommendation and community opportunities that eBay and Etsy have. But Instacart is also a real-time logistics platform. So we also have all of the same fascinating forecasting, scheduling, operations and fulfillment opportunities that Uber or Lyft have.
But why stop there? Our shoppers have to pick complex grocery orders in chaotic store environments, and provide amazing customer service in the moment to our consumers. We also have strong partnerships with great retailers like Whole Foods and with the largest CPG companies in the world which create huge opportunities for new revenue streams driven by our data.
I can’t imagine ever getting bored here, nor will I ever struggle to articulate the amazing things that talented data scientists could do after joining our team. But a great product that I love to use and a cornucopia of fascinating data science challenges wouldn’t have been enough. What won me over and merited our leaving Brooklyn was the people I met here and the culture I have experienced at Instacart.
Instacart is full of incredibly talented individuals who move fast and are laser focused on executing our strategy. But perhaps the most important value that Instacart was founded upon is humility. No matter how talented someone might be, if they won’t listen to the ideas of others or work well in a tight-knit and collegial small team, they aren’t hired. This value is lived all the way to the top, and is embraced by everyone I have met here. I love it and believe it will be critical to our success.
How many investable unicorns are filled with humble people creating products that users love and overflowing with complex and impactful data science problems? I don’t know, but I’m thrilled to have found one.
