Description

Data Scientist / Machine learning Enthusiast with (i) years of data science experience gained at research projects at CTTC Barcelona, NII Tokyo, Telefonica TID Barcelona, AIT Vienna, CTU Prague and academia funded projects; (ii) strong engineering background perfected at Verizon, ATT, HP, etc.

My Github includes code for the following projects:

data analysis using stream-based machine learning approaches

For more detailed information please visit my personal web * application of unsupervised density-based clustering algorithms

template solutions for monitoring of Kafka data streams and application of prediction approaches in Dockerized environment
application of Deep Learning data imputation techniques
adjustments to NLP libraries used by Wikimedia ORES team to predict/classify the quality of articles and for CJK tokenization
utilization of PySpark for data mining in data streams

I have also experience with:

server administration
dockerized environment
using Azure DevOps in the enterprise environment
machine learning pipeline/lifecycle
configuration of routers/switches and loadbalancers in LAN/WAN

For more detailed information see pavol-mulinka.eu

Languages

Slovak
Native or bilingual
English
Fluent
Spanish
Basic
Czech
Native or bilingual

Workplace preferences

Can work on-site

Barcelona (up to 50km)

Other previous positions
(i) Data Scientist (O2 Telefonica - Barcelona, ES); (ii) Data Scientist (AIT - Vienna, AU); (iii) Network Engineer (VSHosting - Prague, CZ); (iv) Network Consulting Engineer (Verizon - Prague, CZ); (v) Senior System Engineer (AT&T - Bratislava, SK); (vi) HP Radia Specialist / (vii) HP Monitoring Support Specialist / (viii) IT VoIP support specialist (Soitron - Bratislava, SK)
TELECOMMUNICATIONS
August 2007 - November 2018 (11 years and 4 months)
Czech Technical University
Principal/Co-investigator
TELECOMMUNICATIONS
November 2013 - December 2020 (7 years and 2 months)
Praga, Czechia
Application of ML methods for pattern recognition in diverse datasets, e.g. (i) active Cloud latency measurements, (ii) NW traces, (iii) IoT logs.
List of projects :
• Practical Privacy-Preserving Data Collection and Utilization using Provable Cryptographic Tools
• Privacy Protection and Machine Learning Utilization of IoT Data in Cloud
• Cloud Performance Analysis and Improvement
• Smart-home IoT and Cloud Telemetry Datamining
• Methods Enhancing Work with Cloud Data
NII
Data Scientist (fixed-term 6 months)
TELECOMMUNICATIONS
March 2019 - September 2019 (6 months)
Tokio, Japan
Application of the unsupervised machine learning (ML) approaches to network (NW) traces (MAWI, Darknet). Generalization and improvement of the hierarchical density-based clustering approach to NW measurements interpretation proposed during AIT Vienna internship. Improvement of PySpark ML scripts running in distributed UX server environment. Results were summarized in conference papers