Based in Waltham, Mass., I’m a data scientist focused on pharmaceutical and medical projects. When I’m not conversing with clients about the complexities of the pharmaceutical value chain, I like to explore the outdoors, try new food and drinks (I’m especially partial to bourbon, red wine, and craft beer), and listen to live music. No two days at McKinsey are exactly the same, but here’s a typical one:
7:15 am: I wake up and have a cup (or two) of coffee while I check my emails and read the news, to make sure I’m up to date on the latest healthcare industry trends and stories. I am an inbox-zero zealot, so the only emails I have in the morning are those that came in after I went to bed.
7:45 am: I travel to work. If I’m supporting a client directly, I will travel from the hotel to the client site. Since I am typically working on multiple engagements simultaneously, sometimes I travel to multiple clients in a single week. Depending on the engagement, I also support client teams remotely, from my home office outside of Boston. Today, I am on-site with a client.
8:30 am: I join a McKinsey problem-solving session with the team on-site. We are helping our client define their contracting strategy for an upcoming product launch. We discuss the initial outputs from a simulation that ran last night in preparation to present our findings at a workshop next week. It’s a complex topic so we focus on making sure we can describe and synthesize the results in simple, straightforward terms.
10:00 am: I spend a few hours running simple regressions and testing initial hypotheses for a separate project I’ve been working on: building a new way to analyze Medicare and Medicaid insurance plans to identify correlations between cost management strategies and plan quality. Ultimately, this type of analysis could be built into our McKinsey online data platforms to help our clients understand the market and develop better commercial strategies.
12:00 pm: I eat lunch with my team. When I am not on-site, I usually eat with my colleagues in Waltham. On Fridays the Waltham office has a catered lunch, which provides a great opportunity to socialize and swap stories, especially with colleagues who focus on other industries or functions.
1:00 pm: I have a progress review call with another client I am supporting. We are helping this client with a M&A asset scan and using one of our pharma and medical products analytics solutions to quantitatively define market opportunities and downside risks. Since this project is on a very short timeline, the senior client is appreciative we were able to generate insights so quickly.
2:00 pm: I check in with one of my data science colleague to see if she needs any help. She is supporting an entirely separate project with a different client. We work through a tough problem, trying to analyze how product reimbursement varies by geography. We develop a hypothesis and a methodology for running the analytics, and she goes back to her team to iterate further.
3:00 pm: I demo our solution virtually for a new client as part of a proposal, focusing on implementing best practices in contract analytics given changing regulatory and market conditions. Hopefully, we can serve this client and help them transform their business for the future.
4:00 pm: I travel from the client to one of McKinsey’s offices to meet with another team. They are supporting a client in a large deal with a health insurer, and we want to translate our complex economic models into clear and actionable recommendations. I help the team pressure test their assumptions and inputs, and we find some counter-intuitive results — always insightful to bring to clients.
5:00 pm: I join a video-conference kickoff with a client who is new to the space and wants a brainstorming session on creative strategies to play in the market. I provide context on recent market trends and the implications for their strategy, and together we come up with a long list of questions to answer in the upcoming weeks during the engagement.
6:30 pm: I head to the airport, or back to my house if I’m not on-site that week. I listen to some podcasts during the trip, (I like all kinds: political, news, story-based, sports, business) answer a few more emails and clear out my inbox before shutting down for the night.
About Josh
Josh is a data scientist based in Waltham, Mass. Prior to McKinsey, he was a research associate, and a project assistant with a healthcare institute. Josh has a bachelor's from Northeastern University.
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