Emanuel Sommer

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Hi, I’m Emanuel! 👋

I’m a passionate Mathematician & Data Scientist with a proven track record of crafting and deploying Statistical / Machine Learning Models. These range from high-dimensional Financial Risk Models to large-scale Learning-to-Rank Models, serving thousands hourly.

With a B.Sc. & M.Sc. in Mathematics from TUM, I’ve built a strong foundation in Statistics & ML. Following industry experience as an ML Practitioner, I’m now pursuing a PhD in Statistics, focusing on sampling-based inference for Bayesian Neural Networks to advance uncertainty quantification in modern Deep Learning. My research has been published at top venues like ICML and ICLR.

Beyond Data & Tech, I’m an enthusiast for hiking, food, travel, often sharing these experiences with my amazing girlfriend. Thriving on challenges and fueled by collaboration, I’m all about the journey from the beauty of math to impactful innovation, while having fun along the way! 🚀🌍

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Experience

PhD Candiate in Statistics & Machine Learning

Ludwig-Maximilians-Universität München (LMU) | Munich Center for Machine Learning (MCML) | Oct 2023 - Now

• Munich Uncertainty Quantification AI Lab | Department of Statistics

• Research Focus: (Sampling based Inference for ) Bayesian Neural Networks

• Teaching: Graduate & Undergraduate Courses in Statistical Modeling and Machine Learning.

Junior Data Scientist

CHECK24 | Technology Hub | June 2022 - Aug 2023

As a Data Scientist in the central Data Science team TechHub, I was at the forefront of driving customer success by delivering innovative ML solutions for various products within the company.

• Responsibility for the Design, Implementation and Maintenance of E2E Learning-to-rank services serving thousands hourly to effortlessly match customers with the products fitting their needs, constantly innovating and as a result improved conversion significantly.

• Collaboration with stakeholders in cross-functional teams across multiple products required me to be highly flexible and a quick learner, with strong business acumen and strong communication skills.

• Equipment of stakeholders with rich dashboards in order to not only monitor but also to foster the explainability and transparency of the ML services.

• Improved the decision making process with cutting-edge statistical methodology in the realm of sequential A/B testing.

Data Scientist Intern

KPMG | Financial Services Core/ Risk Banking | Sep 2019 - Dez 2019

Data Scientist role on a long-term project at an investment bank.

Planning and implementation of a custom automated data quality testing application for credit risk data in R, Rmarkdown and SQL. The generation of the reports was automated and the whole data quality testing cycle was reduced from 3 weeks (manually) to 4 hours (including review). The designed tests included univariate and multivariate outlier detection as well as other statistical methods.

Teaching Assistant

TUM School of Management | April 2019 - Aug 2019

Tutor for the lecture Statistics for Business Administration.

Intern (Schnupperlehre)

Munich RE | EU/LA Property | Juli 2013 - Aug 2013

Got a first taste of the reinsurance business and of the company.

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Education

M.Sc. in Mathematics in Data Science

Technische Universität München (TUM) | April 2020 - Mai 2022

• Thesis on Estimation and Backtesting of the Expected Shortfall and Value at Risk using Vine Copulas
• Focus on Machine Learning & Statistics
• Passed with Distinction

B.Sc. in Mathematics

Technische Universität München (TUM) | Oct 2016 - March 2020

• Thesis on Sports Data Analytics: Regression and tree based models
• Focus on Statistics, Probability and Finance
• Minor in Economics

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Teaching

Lectures

Course	Semester	University	Level
Deep Learning	Summer 2025	LMU	Master
Advanced Statistical Modeling	Winter 2024/25	LMU	Master
Applied Deep Learning	Summer 2024	LMU	Master
Deep Learning	Summer 2024	LMU	Master
Statistical Modeling	Winter 2023/24	LMU	Bachelor
Statistics for Business Administration	Summer 2022	TUM	Bachelor

Master’s Seminars @ LMU

Seminar	Semester
Dynamical Systems in Deep Learning	Summer 2025
Theoretical Foundations of Deep Learning	Winter 2024/25
Uncertainty Quantification in Deep Learning	Summer 2024
Statistical Inference in Data Science	Winter 2023/24

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Tech Stack

Below you can find a selection of my most valued tools from my tech stack roughly grouped by topic. Most tools are open source packages from my two main programming languages & Python . My command of both of them is advanced.

• Data Management
  • SQL
  • S3 & Athena
• Data Exploration & Wrangling
  • pandas, numpy, dbt
  • tidyverse
• Machine Learning & Statistical Modeling
  • Deep Learning: jax, pytorch, tensorflow, torch
  • Statistical Modeling: tidymodels, sklearn, lightgbm, numpyro
  • NLP: tidytext, nltk, fasttext
• MLOps
  • Metaflow
  • MLflow
• Model Deployment
  • APIs: FastAPI, plumber
  • ECS, Copilot, CloudWatch
• Dashboarding & Visualization
  • shiny, golem
  • streamlit
  • Viz: ggplot2, plotly, matplotlib, datashader
• Package & Software Development
  • Version Control & Collaboration: Git
  • package development: devtools, testthat
  • Docker (compose)
  • Utils: pre-commit
• Others
  • LaTeX
  • MS Office