Solution-oriented and technically advanced professional with extensive experience in project management and software development with a strong acumen of data analysis and machine learning. Develop expertise in advanced machine learning techniques and algorithms as well as models’ deployment across a production environment. Possess a strong aptitude to organize data, identify patterns and insights, draw meaningful conclusions, and clearly communicate critical findings. Skilled in formulating and executing effective methods/processes for simulation-based release and statistical model validation. Proficient in uncovering and troubleshooting varied data management issues.
Contents
Professional Experience
(since 2020)
Systems Engineer
Systems Engineering
Bosch Automotive Steering GmbH
Schwäbisch Gmünd
I’m currently the team’s data scientist in training and provide the team with data insights for operational design definition (ODD). I additionally support the software development capabilities of the team by providing the ability to use continuous integration methods for the team.
The most valuable contributions from my side are:
- Developed the ETL process for customer time series batches from the vehicle’s CAN traces to Bosch’s database while serving as a Data Engineer. The database is currently 1 TB in size and growing.
- Developed a measurement analyzing tool and, most importantly, embedded it in a well-documented continuous integration environment so that every Bosch employee can contribute following the Bosch Inner Source philosophy.
- In my first Data Science Project, I developed a neural net that clusters time series data.
(2017-2020)
Development Engineer
Corporate Research
Robert Bosch GmbH
Renningen
Formulated and implemented effective methods/processes for simulation-based release and statistical model validation. Enabled successful execution of projects in an agile setting across different business units of Bosch. Collaborated with an agile Bosch-wide team comprising people from different business units, including but not limited to power tools, electric drives, power systems, brake systems, corporate research, and automotive steering.
- During the project, I had to coordinate efforts with Bosch corporate research management, reporting directly to top management every two months.
- During that time, I acquired inclusive insights into agile methodologies, as well as received a scrum master certificate and gained knowledge as a scrum master proxy.
- Established a toolbox for a simulation and model quality framework currently serving within the Bosch Inner Source Hub.
(2012-2017)
Calculation Engineer
Computer Aided Engineering
Bosch Automotive Steering GmbH
Schwäbisch Gmünd
- Calculated and mechanically designed rack pinion gears.
- Ported ball nut gear dimensioning onto a new platform.
- Performed NVH calculations on steering columns.
- Served as a Task Force Leader to identify and promptly troubleshoot NVH problems in the industrial production of steering columns in Bremen.
(2010-2012)
Project Engineer
MesH Engineering Team
Contract Engineer at Daimler AG
Stuttgart
- Carried out calculations of powertrain oscillations of Daimler light commercial vehicles using SimulationX and Simpack to manage NVH issues.
- Drove chassis optimization based on dynamic simulations for the Formula student racing team of the University of Stuttgart.
- Collaborated with various cross-functional teams to streamline daily operations.
- Executed a MATLAB program to analyze CAN traces and integrate evaluation into a PDF document by utilizing LaTeX.
Programming Experience⇧
(since 2019)
Python and Jupyter
Utilized Python, Jupyter, and Pandas within a data sharing project with an OEM:
- Developed the ETL process for customer data batches from OEM vehicles to Bosch’s database.
- Gathered data for common domain-specific incidents to refine system requirements and models.
- Utilized a machine learning algorithm to cluster the time-variant data.
(2018-2019)
Python and Pandas
Developed a simulation quality framework based on the suggestion of Oberkampf & Roy during a corporate research project:
- Used Pandas, Matplotlib, and Scikit-learn for the visualization of statistical validation data.
- Applied Bitbucket and Jenkins for code peer reviews and automated build/test processes.
- Acquired new resources through the Bosch Inner Source Hub.
(2015)
NX NASTRAN
Generated an FE model analyzer with MATLAB for quality tests of NVH models:
- Analyzed script via the NASTRAN source of the model and deduced the common volume elements and connectors.
- Produced a Bill of Materials with images of the parts, listing density, mass, and stiffness, which led to the creation of a visualization of the parts and the connectors (springs, stiff couplings).
(2009)
VBA
Formulated a depth-first search algorithm in Visual Basic in CATIA during a working student activity:
- Examined the structure of a cable harness utilizing a (self-made) depth-first search algorithm.
- Automatically created a technical drawing with all connectors and cable lengths.
(2007)
ANSYS
Using the APDL script language, combined electrodynamic and structural mechanics within an FE model in ANSYS for my study work:
- Calculated the electromagnetic forces on the injector through the FE mesh.
- Measured mechanical forces while moving the parts, re-meshing the injector, defining new initial conditions, and reiterating starting with the electromagnetic forces.
Education⇧
(2022-2023)
Bosch Senior Data Scientist
The Bosch internal 1.5-year course gave me a deep expert knowledge of Data Science topics, with a large focus on hands-on knowledge and project work within my department, accompanied by Bosch BCAI mentors.
The course is divided into two parts: Junior Expert and Senior Expert. Both parts consist of a “classroom” training phase (online since Corona) and a project phase. The topics covered in the junior’s classroom phase are:
- Basics of Statistics
- Statistical Learning & Gaussian Processes
- Classical Machine Learning
- Deep Learning
- Advanced Deep Learning
- Time Series Analysis
And in the senior’s classroom phase:
- ML Deployment
- Embedded AI
- Validation & Verification / Explainable AI
- Data Efficiency
- Option 1: Sequence Models specialization
- Option 2: Computer Vision specialization
- Foundation models
- Reinforcement Learning (optional)
(2021)
Machine Learning Engineer Nanodegree
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry. [Udacity]
I took a liking to the Udacity didactic, and as part of a Bosch/AS project, I could advance within the field of machine learning. So I completed this Nanodegree at the beginning of the year.
The capstone project, the final test for the Nanodegree, involved creating and training a CNN to detect dog breeds with >80% accuracy. It is hosted in a GitHub repository.
(2020)
Data Analyst Nanodegree
This program prepares you for a career as a data analyst by helping you learn to organize data, uncover patterns and insights, draw meaningful conclusions, and clearly communicate critical findings. You’ll develop proficiency in Python and its data analysis libraries (Numpy, pandas, Matplotlib) and SQL as you build a portfolio of projects to showcase in your job search. [Udacity]
During the COVID-19 crisis, while I was on short-time work, I invested my private time into the Udacity Data Analyst Nanodegree (and this website). I completed the course in June. I learned how to use Jupyter Notebooks, Matplotlib, Seaborne, and most importantly, the Statsmodels toolbox. The course is founded on two pillars: data analysis and results communication, both mastered with the correct use of different hypothesis testing methods and profound usage of the plot capabilities of Python.
The projects I completed during the Udacity Nanodegree are hosted in a GitHub repository.
(2003-2009)
Mechanical Engineering
Dipl.-Ing. Automotive and Engine Engineering
University of Stuttgart
Stuttgart
Majors: Vehicle dynamics and simulation technique
Diploma Thesis: Modelling and Simulation of an automated heavy-duty gearbox
I wrote my diploma thesis as an on-site performer at Daimler AG, where I modeled and simulated a 16-gear transmission for heavy-duty vehicles within Matlab and SimulationX. The goal of the model was to increase the quality of the application of a so-called countershaft brake to reduce shift speed and jerk.
(2002-2003)
Civil Service
Association for Humans with Disabilities e.V.
Nuremberg
During my civil service, I learned to take responsibility for myself, people, and my community. I helped disabled children and frail elders by commuting them to school, for shopping, or to a doctor’s appointment safely, reliably, and in a friendly way.
(until 2002)
School
Wilhelm Löhe School
Nuremberg
A-Levels: Qualification for university entrance with the majors in mathematics, geography and chemistry.
At school I engaged myself as a photographer at the schools gazette and the choir.