Hi, I am Joachim Talloen

I'm a Data Scientist at McKinsey & Co leveraging (Gen)AI and advanced analytics to solve people and organizational problems. In particular, by utilizing analytical techniques—such as supervised and unsupervised machine learning, natural language processing, and econometric methods—I enhance our insights, expand our knowledge, develop solutions, and advise institutions.

I provide guidance on the latest, most pressing global topics, with a particular emphasis on, and passion for: technological productivity improvements, empowering human-(Gen) A.I. collaboration, managing rapid organizational growth, and fostering enduring behavior change.

Prior to joining McKinsey & Co. I completed a Ph.D. in Behavioral Economics at Carnegie Mellon University, where I worked primarily with my advisor Alex Imas. I also spent considerable time at the Machine Learning Department and the Language Technology Institute, collaborating on research with affiliated faculty and taking masters and doctoral-level machine learning and deep learning courses.

Publications

Throughout my time at McKinsey & Co. and Carnegie Mellon I published a myriad of articles in my areas of interest and passion. A selection of the articles I have worked on are listed below:

• To Create Lasting Change, Companies Can Draw on Behavioral Insights
  with Davis Carlin, Kourosh Houshmand, and Sebastian Rodriguez.
  McKinsey & Co., 2023.

• Married to the Job No More: Craving Flexibility, Parents Are Quitting to Get It
  with Aaron De Smet, Bonnie Dowling, and Marino Mugayar-Baldocchi.
  McKinsey & Co., 2021.

• A 680,000-person Megastudy of Nudges to Encourage Vaccination in Pharmacies
  with Katherine L. Milkman, Linnea Gandhi, et al.
  Proceedings of the National Academy of Sciences, 2022, 119(6).

• Megastudies Improve the Impact of Applied Behavioural Science
  with Katherine L. Milkman, Dena Gromet, et al.
  Nature, 2021, 600: 478–483.

Code

I have worked on several interesting AI and data science projects, most notably with Zachary Lipton, Machine Learning professor at CMU and CTO and CSO at Abridge, on the speech-to-text tranformer model Abridge leverages to transcribe and capture noteworthy information from doctor-patient conversations. An interim write up of this work can be found here with the respective python script linked below.

Clinical BERT

Speech-to-text transformer model leveraging clinical BERT with context, in collaboration with Abridge.

Sequence-to-sequence model

End-to-end sequence-to-sequence speech-to-text model with attention.

Multi-layer perceptron

Python code illustrating how to build a simple MLP model using PyTorch.

RCT analysis

Fixed effects and SE clustered regression analyses for an RCT with three conditions.