Viktor S.

Background

About Viktor S.

Experienced data scientist with a strong background in Biotechnology and Bioprocess Engineering. Proficient in R, Python, Business Intelligence and Statistical Software, Data Science Project Management, DMAIC Green Belt Philosophy. Projects are focused on digitization, data standardization, root cause analysis and modeling to summarize and present results gained through data analysis. Published in the fields of statistical analysis and simulation of complex datasets, integration of existing data sources, prioritization of critical business targets and scientific reasoning. Worked on a wide range of problems and data including: a general-purpose data science methodology to support data science projects, bioprocess data analysis from high-throughput equipment for improved process understanding, mathematical process simulation to evaluate the impact of uncertainty, text analysis of commercial production plant data to reduce reporting load, sentiment analysis in questionnaires and literature to understand the workforce, computational fluid dynamics to support scale down model justification, equivalence testing (TOST) to quantify similarity of processes at different scales. Led the section in Data Science & Methodology, which allowed us to formalize well-known steps in a data science project to efficiently identify and meet critical project requirements. My team mapped, integrated and scaled up the data science process in an iterative and agile development cycle. I am excited about cloud technology (AWS, GCP, IBM) and their utilization and application in a classical industrial setting.