AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Science, Machine Learning Professional in Time Series Domain
I am a highly skilled Data Scientist and Machine Learning Specialist with a strong mathematical and technical background. Since 2008, I have been leveraging advanced statistical learning theory, machine learning, deep learning, and data mining techniques to solve complex, real-world problems. My expertise spans a wide range of data-driven approaches, including: - Clustering - Classification - Time series forecasting - Regression analysis - Text/topic mining - Graph analysis - Data visualization - And more In recent years, I have focused extensively on time-series-related projects, tackling tasks such as prediction, classification, and the detection of anomalous behaviors. Throughout my career, I’ve taken on various roles such as Senior Data Scientist, Machine Learning Engineer, Team Lead, and Machine Learning Architect. Some of my notable projects include: - Anomaly Detection in NetFlow Traffic: Strengthening network security by identifying and analyzing -irregularities in data flows. - Early Detection Systems for Water Leakage: Developing residential systems that leverage sensor data to prevent damage through early alerts. - Behavioral Monitoring in Smart IoT Beehives: Using IoT technology to monitor and assess hive health and activity in real-time. - Anti-Theft Systems for Boats: Employing GPS signal processing to detect and prevent unauthorized use or theft of boats. I primarily work within Python and R environments, but I also have experience using Matlab and Julia for certain projects. Additionally, I am proficient in database management and have developed user-friendly applications with frameworks such as Dash and Shiny. What story does your data tell?
Czech Technical University in Prague
Ing., Software Engineering in Economy
January 1, 2008 – January 1, 2012
Czech Technical University in Prague
Bc., Mathematical Modeling
January 1, 2003 – January 1, 2007
Gymnázium J.A.K., Nové Strašecí
Přírodní vědy
January 1, 1998 – January 1, 2002
Everpure
Senior Data Scientist
April 1, 2025 – Present
Prague, Czechia · On-site
GEA Space s.r.o.
Senior Artificial Intelligence Engineer
January 1, 2021 – May 1, 2023
Prague, Czechia
CGI
Machine Learning Architect
June 1, 2019 – December 1, 2020
Prague
Blindspot Solutions
Machine Learning Systems Architect
November 1, 2018 – June 1, 2019
Prague
Vykonia
Data Scientist / Machine Learning Specialist
January 1, 2017 – February 1, 2018
Hlavní město Praha, Česká republika
EY
Senior Data Scientist
March 1, 2016 – February 1, 2018
PwC
Internship at FTS
March 1, 2011 – August 1, 2011
Omnicom Media Group
Data Analyst / Excel VBA programmer
August 1, 2008 – February 1, 2018
Freelancer
Data Science, Machine Learning Professional
August 1, 2008 – Present
OMD Czech
Data Analyst
October 1, 2007 – July 1, 2008
Repository template for Data Science/Machine Learning project
September 1, 2020 – Present
Over the years, I have developed a repository template that showcases my experience and approach to data science projects. This template serves as a foundation to streamline and improve the efficiency of future projects. This is an ongoing project that I continuously update after each new engagement to further enhance its capabilities. The repository currently includes: - Installation guidelines - Environment handling (libraries and version management) - Code quality tools (mypy, pylint, unit tests, pytest) - Makefiles to simplify workflows - Environment setup and configuration scripts - Jupyter notebook templates designed for software development practices - Visualizations tailored for time-series-related projects - A Python Dash visualization dashboard skeleton, which can be built upon for more advanced features Future updates will focus on expanding MLOps capabilities, including MLFlow, CI/CD pipelines, Kedro, Airflow, and other tools to provide demonstrations of these approaches.
Implementation of Robust Regression Methods
January 1, 2016 – December 1, 2016
Implementation of Robust Regression Methods in R for professor Jan Ámos Víšek, Charles University in Prague, Faculty of Social Sciences. - Whitepapers read and study. - Models implementation in R and MATLAB.
Social Graph Analysis and Visualization
September 1, 2015 – February 1, 2018
Deploy data science processes within the company, analyse social graph, create visualizations and models to answer respective business questions. - Set up data science processes and standards at company and within the team (literate programming, reproducibility, ...). - Perform data cleaning, preprocessing and validation. - Mathematization of the task. - Create advanced graph visualizations using interactive libraries. - Implement POC application in Shiny R. - Analyse created graph with graph-based models. - Apply text and topic mining analysis on textual part of the data. - Enrich the visualization with results of these advanced models. - Design database for company data storage.
Classification of good and bad information sources
June 1, 2014 – March 1, 2016
Classification of good and bad information sources for Semantic Visions company. - Set up data science processes and standards at company and within the team (literate programming, reproducibility), project management. - Perform data cleaning, preprocessing and validation. - Feature engineering, dimensionality reduction. - Apply classification models; logistic regression, SVM, neural networks, tree-based models, ...
Introduction to Big Data
Coursera Course Certificates
June 24, 2026 – Present
Big Data Integration and Processing
Coursera Course Certificates
June 24, 2026 – Present
Text Retrieval and Search Engines
Coursera Course Certificates
June 24, 2026 – Present
Introduction to Big Data Analytics
Coursera Course Certificates
June 24, 2026 – Present
Developing Data Products
Coursera Course Certificates
June 24, 2026 – Present
Statistical Learning
Stanford University Online
June 24, 2026 – Present
Reproducible Research
Coursera Course Certificates
June 24, 2026 – Present
Python for Data Science
Coursera Course Certificates
June 24, 2026 – Present
Deep Learning Specialization
Coursera Course Certificates
June 24, 2026 – Present
Bayesian Machine Learning in Python: A/B Testing
Udemy
June 24, 2026 – Present
30 Day Challenge to a More Productive and Much Happier You
Udemy
June 24, 2026 – Present
Jumpstart to Docker, from zero to hero
Udemy
June 24, 2026 – Present
Complete Python Bootcamp: Go from zero to hero in Python
Udemy
June 24, 2026 – Present
Convolutional Neural Networks
Coursera Course Certificates
June 24, 2026 – Present
Executive Data Science Capstone
Coursera Course Certificates
June 24, 2026 – Present
Data Science Capstone
Coursera Course Certificates
June 24, 2026 – Present
Operations Analytics
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera Course Certificates
June 24, 2026 – Present
Regression Models
Coursera Course Certificates
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
AI For Everyone
Coursera Course Certificates
June 24, 2026 – Present
What is Data Science?
Coursera Course Certificates
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera Course Certificates
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera Course Certificates
June 24, 2026 – Present
Data Science in Real Life
Coursera Course Certificates
June 24, 2026 – Present
A Crash Course in Data Science
Coursera Course Certificates
June 24, 2026 – Present
Executive Data Science Specialization
Coursera Course Certificates
June 24, 2026 – Present
Customer Analytics
Coursera Course Certificates
June 24, 2026 – Present
Getting and Cleaning Data
Coursera Course Certificates
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera Course Certificates
June 24, 2026 – Present
Cluster Analysis in Data Mining
Coursera Course Certificates
June 24, 2026 – Present
DeepLearning.AI TensorFlow Developer DeepLearning.AI
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning Engineering for Production (MLOps)
Coursera Course Certificates
June 24, 2026 – Present
Open Source tools for Data Science
Coursera Course Certificates
June 24, 2026 – Present
Data Analysis with Python
Coursera Course Certificates
June 24, 2026 – Present
Data Science Methodology
Coursera Course Certificates
June 24, 2026 – Present
Sequence Models
Coursera Course Certificates
June 24, 2026 – Present
Julia for Data Science
Udemy
June 24, 2026 – Present
Deep Learning Prerequisites: Logistic Regression in Python
Udemy
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera Course Certificates
June 24, 2026 – Present
Big Data Modeling and Management Systems
Coursera Course Certificates
June 24, 2026 – Present
Text Mining and Analytics (with Honors)
Coursera Course Certificates
June 24, 2026 – Present
Pattern Discovery in Data Mining
Coursera Course Certificates
June 24, 2026 – Present
Data Science Specialization
Coursera Course Certificates
June 24, 2026 – Present
Hadoop Platform and Application Framework
Coursera Course Certificates
June 24, 2026 – Present
Executive Data Science Specialization
Johns Hopkins University
June 24, 2026 – Present
Data Visualization
Coursera Course Certificates
June 24, 2026 – Present
Practical Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Exploratory Data Analysis
Coursera Course Certificates
June 24, 2026 – Present
TensorFlow: Data and Deployment
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning with Python
Coursera Course Certificates
June 24, 2026 – Present
Data Visualization with Python
Coursera Course Certificates
June 24, 2026 – Present
Neural Networks for Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Deep Learning Foundation Nanodegree Program
Udacity
June 24, 2026 – Present
Deep Learning Prerequisites: Linear Regression in Python
Udemy
June 24, 2026 – Present
Introduction to Mathematical Thinking
Coursera Course Certificates
June 24, 2026 – Present
Graph Analytics for Big Data (2015)
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning With Big Data (2015)
Coursera Course Certificates
June 24, 2026 – Present
Building a Data Science Team
Coursera Course Certificates
June 24, 2026 – Present
Managing Data Analysis
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project experience, ranging from academic implementations to enterprise-level ML architecture, demonstrates adaptability and a broad interest in various applications of ML. Their emphasis on establishing data science standards, reproducibility, and team education aligns well with a culture that values best practices and knowledge sharing. The continuous learning through numerous certifications also indicates a proactive and growth-oriented mindset. The 'Repository template for Data Science/Machine Learning project' shows a strong inclination towards open-source contributions and community engagement, which can be a positive cultural fit.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in setting up data science processes, team management, and client-facing roles, indicating strong problem-solving, communication, and analytical skills. The focus on 'treating analytics as a product' and 'closing the loop between strategy, design, and delivered analytics' suggests a product-oriented mindset and operational awareness. The continuous updates to a personal repository template also show initiative and a commitment to best practices.