Data Scientist | Machine Learning Engineer | NLP
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Assessing your cultural and operational fit
Ludwig-Maximilians-Universität München
Doctor of Philosophy - PhD, Applied Linguistics
March 1, 2019 – October 1, 2022
Technical University of Munich
Master’s Degree, Informatics
January 1, 2013 – January 1, 2016
National University of Sciences and Technology (NUST)
Bachelor’s Degree, Software Engineering
January 1, 2008 – January 1, 2012
Siemens
Senior Software Engineer
February 1, 2024 – Present
Munich, Bavaria, Germany · On-site
Veeva Systems
Data Scientist
January 1, 2023 – February 1, 2024
Munich, Bavaria, Germany · Remote
Siemens
Researcher Phd Candidate
March 1, 2019 – September 1, 2022
Munich Area, Germany
SAP
Lead Data Scientist
April 1, 2018 – February 1, 2019
SAP
Machine Learning Engineer
November 1, 2017 – March 1, 2018
Siemens
Freelancer
April 1, 2017 – September 1, 2017
Munich Area, Germany
Ippen Digital Media GmbH
Working Student (Machine Learning)
November 1, 2015 – September 1, 2016
Munich Area, Germany
Intel Corporation
Working Student (Software Engineer)
October 1, 2013 – August 1, 2015
Munich Area, Germany
Technical University of Munich
Graduate Researcher
July 1, 2013 – September 1, 2013
Greater Munich Metropolitan Area
TeReSol
Software Engineer
July 1, 2012 – March 1, 2013
Rawalpindi, Pakistan
Master Thesis: Deep Learning for Question Answering Systems
January 1, 2016 – July 1, 2016
• Explored Recurrent Neural Networks (RNNs), Long short-term memory (LSTM), Gated recurrent units (GRUs), Attention models & Memory Networks for Question Answering task on Facebook's bAbI dataset. Advisor: Dr. David Kaumanns Supervisors: Prof. Dr. Patrick van der Smagt, Prof. Dr. Hinrich Schütze
Cultural Fit Analysis
The candidate has a strong academic background and significant industry experience across various companies (Siemens, Veeva Systems, SAP, Intel). Their work spans research (PhD, Siemens Researcher) and product development (Veeva, SAP), indicating adaptability to different organizational cultures. The focus on cutting-edge AI/ML technologies aligns well with innovative and research-driven environments. However, the target role is 'ML Engineer' while much of the experience is 'Data Scientist' or 'Researcher', which might imply a slightly different focus on engineering rigor versus pure model development/research. The project diversity is good, but the specific alignment with a pure ML Engineering role needs further validation.
Soft Skills & Operational Fit
The candidate's resume indicates strong collaboration skills through working with internal data curation teams, business managers, and leading a team of data scientists. Their participation in competitive shared tasks suggests a drive for excellence and problem-solving under pressure. The diverse project experience, from academic research to industrial applications, points to adaptability and a broad operational fit within ML/AI teams.