Nicolae Righeriu
Bis 2019, Msc. Data science, Technische Universiteit Eindhoven and Universidad Politecnica de Madrid
München, Deutschland
Werdegang
Berufserfahrung von Nicolae Righeriu
Bis heute 3 Jahre und 10 Monate, seit Sep. 2020
Data engineer
Allianz Digital Health
10 Monate, Nov. 2019 - Aug. 2020
Software Engineering Consultant
AKKA Consulting GmbH
- Maintaining and extending a MS-Access database - Courses on project management and experience with agile methods - Participating in workshops about soft-and consulting skills - Developing innovation projects with intercultural teams in various fields
7 Monate, Jan. 2019 - Juli 2019
Data Science Intern
Vizzuality
-Building a global land cover classifier using recent satellite images -Writing and testing microservices for parallel computation using Google Earth Engine, Python -Geodata processing with geopandas: tidying, processing or simplifying raw geographical formats -Performing analyses and creating statistical graphics to expose data in a usable manner
-Programming a Java GUI for recording simulation results in databases -Building a Java tool for registering data tags in C2Mon -Software based actuation of hardware components in the lab
7 Monate, März 2016 - Sep. 2016
Software tester
Intellic GmbH
-Writing and automating test cases in Python while analyzing product requirements -Ensuring product quality
Ausbildung von Nicolae Righeriu
2 Jahre, Sep. 2017 - Aug. 2019
Msc. Data science
Technische Universiteit Eindhoven and Universidad Politecnica de Madrid
Master of Science with a third of entrepreneurship. Subjects such as Process mining, Data Engineering, Large Scale Data Management, Applied statistics, Time series forecasting, Innovation & Entrepreneurship, Massively Parallel Machine Learning, Data Analysis.
3 Jahre und 11 Monate, Sep. 2013 - Juli 2017
Computer Science
TU Berlin
Artificial intelligence, Data Analysis, Stochastics, Data structures, Logic, Computer networks, Scientific Computing, Computer architecture, System programming. Thesis: Development of a hybrid filter-wrapper feature selection approach for short-term load forecasting
Sprachen
Deutsch
Fließend
Englisch
Fließend
Rumänisch
Muttersprache
Spanisch
Fließend
Niederländisch
Grundlagen