Daniel Schalk

Dr. Daniel Schalk

Statistician / Data Scientist

February 24, 1994

Personal Profile

I am statistician / data scientist with strong interests in machine learning, statistical modelling, programming, dashboarding, automated reporting, reproducibility, and data visualization. Besides the theoretical foundations of my research, I am also enthusiastic about the practical applications of statistics and machine learning. As such, I am an author and contributor to several R packages and have successfully competed in data science contests.


Statistics / Data Science, Ph.D.

LMU Munich, Grade: Magna cum laude (1.0)

June 2018 - March 2023

Thesis: Modern approaches for component-wise boosting: Automation, efficiency, and distributed computing with application to the medical domain

  • Research focus: Modern approaches of component-wise boosting and distributed computing/federated learning.
  • Responsible for the courses Introduction to Machine Learning, Predictive Modeling, and Advanced Programming with R".
  • Development and integration of interactive teaching websites.

Statistics, M.Sc.

LMU Munich, Grade: 1.02

April 2016 - Mai 2018

Thesis: Efficient and Distributed Model-Based Boosting for Large Datasets.   -   Grade: 1.0

Business Mathematics and Actuarial Sciences, B.Sc.

University of Applied Sciences Rosenheim, Grade: 2.1

October 2012 - April 2016

Thesis: Parameteridentifikation mittels linearer und nicht-linearer Regressionsmodelle zur Bestimmung thermischer Kennwerte. (en: Parameter identification using linear and non-linear regression models to determine thermal characteristics.)   -   Grade: 1.0

Work Experience


Essential Data Science Training (former Munich R Courses)

February 2018 - Present

  • Trainer for several courses (i.a. Machine Learning Bootcamp, Unsupervised Learning, Programming in R, or Statistik Grundlagenkurs).
  • Creation of teaching material.

Research Assistant

LMU Munich

March 2017 - Mai 2018

  • Implementation of a framework for dynamic and modular slide creation.
  • Assisting in the creation of lecture material.
  • Assistant for consulting projects.
  • Organizing and assisting courses for external clients.

Consulting Project

Munich Re

October 2016 - November 2017

  • Estimation and validation of transition probabilities between customer states using machine learning algorithms and statistical models.
  • Visualisation of the results by providing an interactive web application written in R using shiny.

Research Assistant

University of Applied Sciences Rosenheim

August 2015 - October 2016

  • Modeling and estimation of thermal characteristics under consideration of time dependencies.
  • Estimation of residuals of motor vehicle damages in non-life insurance.
  • Implementation, validation, and runtime optimization using R.


Stat-Up Munich

March 2015 - Juli 2015

  • Data analyses in several project.
  • Creation of training courses, among others for data mining.


Software Projects


1st place at the AI Hackathon as part of the ai.bay 2023

February 2023

Best statistics graduate from LMU in the year 2018/19

Mai 2019

MRDataThon 2017 Best Overall Solution

November 2017

Scholarship "Deutschlandstipendium"

April 2017 - October 2017

TEFDataChellenge 2017 Best Overall Solution

October 2017

DataFest Germany 2017 Best Visualisation

April 2017