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Senior Machine Learning Research Scientist at Carl ZEISS AG, Corporate Research and Technology.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
ZEISS
Data Scientist
June 26, 2026 – Present
n2v
January 11, 2019 – December 18, 2024
This is the implementation of Noise2Void training.
View ProjectconditionalGAN
November 12, 2018 – November 12, 2018
A Generative Adversarial Network which expresses the random vector as art as well es non-art simultaneously
View Projectrnn-lstm-example
August 2, 2017 – August 2, 2017
Implementation of an RNN and an LSTM with no DL libraries used, numpy only
View Projectdbn-autoencoder
July 30, 2017 – July 30, 2017
Unsupervised Neural Coding of Nightingale Songs Using Deep Autoencoders, pretrained as Restricted Boltzmann Machines
View Projectheraldic
July 29, 2017 – April 5, 2019
Automatically extract single images of heraldry from scanned book pages by detecting the grid and predicting missing values using regression.
View Projecttensorflow_examples
July 28, 2017 – August 24, 2017
tensorflow_examples — GitHub repository
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong inclination towards independent research and development in data science and machine learning. The diversity of projects and technologies (Python, C++, Java, Matlab) suggests adaptability and a broad technical curiosity. However, the experience level being 0 while having a current role as a Data Scientist at ZEISS is a significant discrepancy that impacts cultural fit assessment, as it's unclear if this is an entry-level or more senior role. The projects are all personal, which doesn't provide insight into team collaboration or corporate environment fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's experience level is listed as 0, but they have a current role as a Data Scientist at ZEISS, which is contradictory. Project descriptions are concise but lack detail on collaboration or problem-solving approaches.