I am excited when I find new insights in previously unused data. And when we package it into a software app that our client can use going forward, it is so rewarding to see the solution come to life.
Current role at McKinsey
I am an associate partner responsible for our Swiss analytics hub. I work primarily with retailers and industrials.
Choosing McKinsey
Before joining McKinsey, I was a postdoctoral researcher studying computer vision—mostly urban scene analysis for autonomous vehicles—at ETH Zurich. I joined McKinsey to get closer to business problems in a fast-changing environment. Because projects typically last around three to six months, you get to see a lot in a short time. When I first joined, data analytics did not exist. As it formed in recent years, I saw the ideal opportunity to combine my passions for data analytics and business.
Client work in analytics
We have two types of analytics projects. One is a standard McKinsey study where analytics is used as a “next-level Excel” and we can derive strategic insights from a much larger database. For example, if we are helping a telecommunications company reduce churn, an analytics consultant would develop the model to predict churn, and the rest of the team would derive potential marketing actions from it. The other is an analytics transformation in which we help clients build their own analytics capabilities.
Driving impact through data
I am excited when I find new insights in previously unused data. And when we package it into a software app that our client can use going forward, it is so rewarding to see the solution come to life.
Advice for aspiring data scientists
- Think business first. Understand what technology is really needed to address the business problem. AI and deep learning are often unnecessary.
- Know the methodologies. Understand the methodologies—including the advantages and shortcomings—in statistical detail.
- Build testbeds. The larger the data, the more important it is to have standardized tests to understand and resolve potential structural issues early on.
Advice for those considering McKinsey Analytics
There has never been a better time to join this practice. The demand for analytics-fueled strategy work and analytics transformations is high. At McKinsey, you can be part of a growing, global community and benefit from trainings in analytics, business, and leadership.
Education
ETH Zurich
PhD, computer vision and machine learning
MSc, computer science