– Hi everyone! My name is Neil and I ’m an analytics fellow in the Waltham Knowledge Center. I joined the firm after earning my master’s degree in data science from Harvard. Currently, I’m working on an engagement in the public sector for a customs organization. I’m literally making a difference in the lives of citizens using analytics. It’s amazing. Here’s what a typical day looks like:
7:00am: Wake up. Run my model on the data that came in overnight and send the results to the client. I’ve spent the last three months building a model that attempts to detect risk at ports of entry. Every morning, this model selects a shipment that it predicts is high risk using anomaly detection, clustering and other advanced machine learning techniques. That shipment is inspected by port agents.
7:30am: Grab breakfast in the rooftop hotel lounge. Catch up on data, US, and local news before I head into the office.
8:30am: Check-in with my team at the client site. We discuss our priorities for the day and talk about pieces of our work that overlap. We strategically think through upcoming meetings and make sure all of the essential team members will be in the right conversations.
9:30am: Meet with Aaron, a senior data scientist, who helps me think through some of the issues I’m facing this week. I recently learned about a new technique in a machine learning journal that I’m anxious to implement; we talk about how to use that approach to improve the model I’m running.
10:30am: My coding time: I like to block out chunks of the day to focus on my model; it helps me focus and ensures I have time to do what I need to do. Still, I love to engage in conversations about other work-streams so occasionally I’ll jump into an ad-hoc problem-solving discussion with my team mates. We work in a very open and collaborative environment.
12:30pm: Lunch in the team room today. We order pizza from a local joint down the street and take a break from work to chow and chat.
1:15pm: One of my clients calls. She is interested in the newest iteration of the model and wants to understand how its results may affect her work. I walk over to her desk so we can talk through it face to face. It’s exciting for me to see our new approach in action. The two of us have a good discussion and I walk away with practical advice for what to do next.
2:00pm: More modeling time. I run into some roadblocks implementing this new feature, but after a few Slack messages and a lot of Googling, I figure it out.
3:30pm: Client meeting. Once a week we meet with the full client team of about 40 people. We discuss any challenges we’ve encountered and the impact we’ve created. Everyone on the team has played a part in our progress and feels integral to our success.
4:30pm: Problem solving with McKinsey leadership. We use this time to engage in healthy and rousing debates about all of our work-streams, which cover different aspects of the customs organization. I talk about my model and the partner on the team shares an insightful suggestion to tweak the model by including another element of data that may also predict risk.
5:30pm: Team check-out. We recap next steps to make sure we ’re all on the same page. We drive back to the hotel, and I retire to my room to catch up with my friends and family. I usually work out at the hotel gym, as well.
7:30pm: Today we have a team dinner at a restaurant. This team is particularly social, and it doesn’t hurt that we get a great view of the sunset dipping behind the horizon from our table. We talk about how we did in the March Madness brackets, our favorite podcasts, and the dreadful tax season. (My photo is of my colleague and friend Dan and I sharing a heart shaped pizza on Valentine ’s Day).
9:00pm: I take some time while I unwind to just play with the data. These are my moments of zen: I wander and explore, not knowing what to expect but always discovering something new and exciting. Before closing up shop, I send a few lighter emails – to catch up with colleagues from past teams or say hello to other data scientists at McKinsey.
11:30pm: I drift off to sleep and dream of numbers.
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