Sr. Data Scientist
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Ich interessiere mich für künstliche Intelligenz, insbesondere für Deep Neural Networks, für statistische Datenanalyse, Computergrafik, Neurowissenschaft, Neurobildgebungsverfahren und Virtual-Reality.
ETH Zürich
Doctor of Sciences (Ph. D.), Neuroinformatik, Department für Elektrotechnik
January 1, 2010 – January 1, 2016
Technical University of Munich
Bachelor of Science, Elektrotechnik und Informationstechnik
January 1, 2004 – January 1, 2009
Technical University of Munich
Diplom Ingenieur, Elektrotechnik und Informationstechnik
January 1, 2004 – January 1, 2010
D ONE – Data Driven Value Creation
Data Consultant
January 1, 2022 – Present
Auterion
Sr. Data Scientist
January 1, 2018 – December 1, 2022
Zurich, Switzerland
PX4
Sr. Data Scientist
September 1, 2017 – December 1, 2022
Zurich, Switzerland
Disney Research
Praktikum
March 1, 2017 – August 1, 2017
Zurich, Switzerland
ETH Zurich
Doktoratsforschung
June 1, 2010 – October 1, 2016
Zürich, Schweiz
BMW Group
Diplomarbeit: Entwicklung und Validierung eines prädiktiven Energiemanagements
July 1, 2009 – January 1, 2010
Greater Munich Metropolitan Area
TESIS DYNAware GmbH
Werkstudent
May 1, 2008 – December 1, 2009
München, Deutschland
The University of Western Australia
Bachelorbeit: Graphics for a 3D Driving Simulator
August 1, 2007 – February 1, 2008
Greater Perth Area
Technische Universität München
Werkstudent
April 1, 2007 – July 1, 2007
Technische Universität München
Werkstudent
May 1, 2006 – December 1, 2006
Kontron
Internship
March 1, 2006 – October 1, 2006
Eching, Germany
Technische Universität München
Ausgewählte Universitätsprojekte
September 1, 2004 – January 1, 2010
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, followed by roles in data science and consulting. While the technical skills align with an ML Engineer role, the breadth of project diversity outside of academic/research settings is somewhat limited in the provided descriptions. The transition from deep academic research to industry roles like 'Data Consultant' and 'Sr. Data Scientist' shows adaptability. However, the lack of detailed project descriptions makes it challenging to fully assess alignment with a fast-paced, product-driven ML engineering culture.
Soft Skills & Operational Fit
The candidate's experience descriptions indicate a strong analytical and problem-solving mindset, essential for an ML Engineer. The Ph.D. research suggests a capacity for independent research and complex system development. Experience in functional safety standards implies an understanding of robust system design and deployment. However, without specific project details or direct communication assessments, it's difficult to fully assess collaboration or leadership skills.