Dmitriy Drichel
Data Scientist
Experience
Freelance Data Scientist
Merck KgaA
Development, testing and benchmarking of bioinformatics workflows in oncological research.
- python, R, bash
- AWS, Genedata Profiler, Rstudio, Gitlab
- germline and somatic variant calling, performance evaluation
- genomics and transcriptomics, data acquisition and data simulation
Freelance Data Scientist
Boehringer Ingelheim (BI-X)
Development and implementation of a genomics/transcriptomics analysis pipeline. Data acquisition, data engineering, data analysis, software development.
- python, R, high-performance computing, AWS, docker
- DevOps, CI/CD
- biomolecular data, sequence analysis, high-dimensional data
- genomics, transcriptomics, epigenomics
- Oxford Nanopore, PacBio, Illumina NGS, microarray technology
- development of novel algorithms and statistical methodologies
- application and product development, stakeholder management
Freelance Application Developer
Miltenyi Biotec
Development of python applications for flow cytometry data acquisition and analysis. Engineering of custom applications for embedded systems.
- python, pandas, scikit-learn, jupyter
- unsupervised machine learning, clustering
- project management, documentation: scrum, agile, jira, confluence
- version control systems: git, svn, jenkins
Freelance Data Scientist
Jahnes GmbH
Implementation of a machine learning model in a leading German bank. Data extraction, data engineering, exploratory and descriptive analysis, visualization.
- python, pandas, scikit-learn, matplotlib, jupyter, SQL
- risk analysis, consumer loans
Postdoc
Cologne Center for Genomics (CCG), University of Cologne
Development of analysis software for big genomic data sets. Development of novel statistical methodology and algorithms, management of small teams.
- C/C++, python, Perl, R
- epidemiological studies, scientific writing
- big data analysis, high-performance computing
Research Associate
German Center for Neurodegenerative Diseases (DZNE)
Development of application software for analysis of biological data. Development and evaluation of novel statistical methodology, high-performance computing.
- C/C++, python, OpenMP
- Evaluation and optimization of data handling pipelines
- Large-scale simulations of biological data sets