Jiří
Pešík

Senior Software Engineer

Jiří Pešík

Career

Work Experience

Senior Software Engineer

Current

Rapid7 · Prague

Part of the Content Development team, expanding the portfolio of products covered by the InsightVM vulnerability detection product. Responsibilities include product research, vendor communication, designing and implementing detection of installed products, developing plug-ins for vendor data processing, testing, and resolving customer requirements.

PythonInsightVMVulnerability DetectionPlugin Development

Software Engineer

Huld · Prague / Pilsen

Worked on a ground control system for CubeSats (OrbitCon) using React and Node.js. Contributed to the Archaeological Map of the Czech Republic and an Automatic Weight Measuring System — both Python/Django web applications. Contributed to technical proposals submitted to the European Space Agency (ESA).

PythonDjangoReactNode.jsESA Proposals

IT Consultant & Data Analyst

Aimtec · Pilsen

Customized and developed plugins for MS Dynamics 365, MS Project Server, and MS SharePoint. Created Excel and Power BI reports, trained new employees, developed data warehouses using SQL Server Analysis Services, and implemented ETL processes using Transact-SQL and SQL Server Integration Services. Gained experience with Microsoft Azure.

MS Dynamics 365Power BISQL ServerSSISSSASAzure

IT Reporting Specialist

Sony DADC · Pilsen

Created and administered database reports using SAP DESKi and SAP WEBi applications, as well as Oracle SQL. Administered BI servers based on Infor BI applications.

SAP BusinessObjectsOracle SQLInfor BI

Software Developer

Sportcentral.cz · Pilsen

Contributed to the development of the Sportcentral web application using PHP and the Nette framework.

PHPNette Framework

Expertise

Skills & Technologies

Python & Data Science

Pythonpandasscipyscikit-learnstatsmodelsElementTree

Web Development

DjangoReact.jsNode.jsJavaScriptjQueryHTML/CSSBootstrap

Data & BI

Power BISQL ServerPostgreSQLPL/SQLMongoDBElasticSearch

DevOps & Cloud

DockerLinuxGitAWSAzure

Enterprise Systems

MS Dynamics 365MS SharePointSSISSSASSSRS

Other

C#JavaPRINCE2ETL ProcessesREST API

Education

Lecturing

Lecturer

Czechitas · Prague

Vibe Coding in Practice: Build Your Own Website with AI Without Programming

Hands-on introduction to building websites using AI tools — no prior programming experience required.

Python Programming

Python course covering dictionaries, functions, OOP, and API integration for data work.

AI Transformation Manager

3-month intensive on planning and leading AI transformation strategies within organizations.

Digital Data Academy

Semester-long data analytics program covering Python, SQL, and data visualization from scratch.

Python for Data Analysis

Data analysis with pandas — data cleaning, visualization, and practical use of AI tools.

Data Science Foundations in Python

Foundational data science concepts and workflows implemented in Python.

Academic

Education

2013

Master's Degree

Financial Informatics and StatisticsUniversity of West Bohemia, Faculty of Applied Sciences

2-year academic program combining advanced statistics and probability (hypothesis testing, multivariate analysis, time series forecasting), financial mathematics (derivatives, portfolio analysis, insurance modelling), mathematical optimization (graph algorithms, operations research), and advanced database systems.

2007

School-Leaving Certificate

Information Technology

High School of Electrical Engineering, Pilsen

Professional

Certifications

PRINCE2 Foundations

POTIFOB · 2022

Power BI Advanced Techniques

GOPAS · 2018

Project Management

Wiseman · 2018

Time Management

Roman Čiviš · 2016

Microsoft Excel Advanced

GOPAS · 2015

Research

Publications

Machine Learning Image Recognition for GNSS Jamming Signals Categorization

J. Steiner, J. Pešík

Neural Network WorldVol. 34, No. 6, pp. 341–360 · 2024

DOI: 10.14311/NNW.2024.34.019

Explores machine learning image recognition for categorizing GNSS jamming signals. Using data from a long-term highway monitoring campaign with over 2,000 jamming events, five ResNet models (18–152 layers) were evaluated — achieving over 90% precision in jamming signal classification.

PythonResNetMachine LearningGNSSSignal Classification