Data Scientist

M. takes pride in building models translating data points into business insights. Passionate for Kaggle challenges, now eager to apply the same knowledge to real-world business problems.

• Business Intelligence (BI)4 J.
• Data Science
• Datenanalyse4 J.
• ETL4 J.
• Keras
• Layoutsoftware
• Lineare Regression4 J.
• Maschinelles Lernen4 J.
• Mathematik4 J.
• Opencv
• Pandas
• Postgresql4 J.
• Power Bi4 J.
• Python4 J.
• R (Programmiersprache)4 J.
• Scikit-learn
• Tableau4 J.
• Tensorflow

Dear Sir/Madame,

I am applying for the aforementioned job position after reading about the job description on the website and it has immediately aroused my interest.
To briefly summarise my educational background, I have studied applied mathematics during my bachelor studies where I took the first steps on familiarising myself with data structure, probability theory, and stochastic processes specifically time-series analysis. During my master studies, I have deepened my knowledge of probability theory were my master’s thesis distinctively addressed the specific properties of Radon measure. During this time I got more oriented towards partial differential equations where I continued further this topic in my doctoral studies at the University of Potsdam under the supervisory of Prof. Dr B.-W. Schulze. We advanced the understanding of the specific type of singularities that appear in
certain partial differential equations.
Lately, I have become interested in applying my knowledge that I have earned during my studies in real-world problems that require data analysis and involve predictions by the means of machine-learning algorithms. In a recent call from the University of Liverpool, I participated in a competition that required us to predict the correlation between the synthesised signal time-series and the transport of ions through
body membranes. These synthesised signals could affect the flow of the ions by opening and closing the porous channels in the membrane. The approach taken in this problem was to apply WaveNet, firstly to
remove the noise in the synthesised signals by using wavelets and perform a regression modelling by the deep neural network. In this particular project I used tensorflow, (Keras) WaveNet implementation.
Also recently, I have been working with Max-Planck Institute for Dynamics of Complex Technical Systems of Magdeburg to apply machine learning algorithms to estimate the social distancing required to control COVID-19 spread. This project is a part of [...] project at Max-Planck institute started in 2018. As a mathematical reduction approach, we are exploring possible modelling candidates to solve an optimisation problem using conventional machine learning algorithms and Deep Neural Networks.
I believe that my knowledge and experience could be leveraged where the benefits of the company is concerned with. I would be glad to have the chance to get better informed about the details of the position and explain in details my expertise in a personal interview. I am looking forward to hearing back from you.

Sincerely
Dr. M. H. M.

Strong analysis and Data science state of art tools and algorithms