I studied statistics at business school, so when I graduated, I sought a position where I could apply my technical skills to business problems. I joined McKinsey’s Chicago office as a data scientist because it felt like the right place to use my strengths. Here, I apply statistics to many different situations, affecting change in some of the world’s most complex organizations, rather than solving a similar type of problem over and over again.
Getting creative with data
In school, I learned the theories that inform which statistical models are best suited for practical application. When I started working, I learned that data infrastructure informs the model you’ll use. For example, I’m currently working on a supply chain project, where we’re deciding what parts of our model to implement in Excel and what we should implement in Python. The goal of the project is to help our client’s planners make better supply and allocation planning decisions.
In this situation, it’s important for the user to make manual overrides, and scenario planning though dynamically updating calculations was best done in Excel. Using Python is great in theory, but we need to use what people are comfortable with, so we have to get creative.
The team I’m working with is a mix of data engineers, data scientists and business consultants. My combined business and tech background helps me connect with the business consultants and communicate to them what’s easy and what’s difficult from a technology standpoint.
I find people at McKinsey are willing to listen to others’ expertise. Everyone approaches problems from a different framework. For example, data scientists might focus more on model accuracy, while business consultants might focus more on client communications.
By working on cross-functional teams, you learn to think like the other roles and communicate the value of your work to others. That has been an important skill because a large part of my job is explaining data science to people–how we use input data, move that data through the model, and use that final model output to make a decision.
Finding my people
I’m lucky to have started at the firm at the same time as an amazing group of peers. I found a mentor on my first project, and have found a few unofficial mentors along the way.
My mentor was my project manager on my first assignment at McKinsey. She advised me on what kinds of projects to look out for, considering the type of work I enjoy and the people and environments that will help me grow. She checks in on me and makes sure I’m happy with my journey here. People here are eager to help however they can.
About Anika
Anika is a data scientist based in Chicago. She earned her bachelor’s in statistics from Wharton School of the University of Pennsylvania. Prior to joining McKinsey, Anika was a business technology analyst at Deloitte.
For more information on McKinsey's tech career paths, visit mckinsey.com/TechCareers.