Lead Data Scientist at Ardigen | CS PhD
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AGH University of Krakow
Doctor of Philosophy - PhD, Computer Science
January 1, 2018 – March 1, 2024
AGH University of Krakow
Master of Science in Engineering, Automatic Control and Robotics
January 1, 2014 – January 1, 2016
AGH University of Krakow
Bachelor of Engineering, Automatic Control and Robotics
January 1, 2010 – January 1, 2014
Ardigen
Lead Data Scientist & Product Owner, AI Consulting Services
January 1, 2025 – Present
Ardigen
Senior Data Scientist & Product Owner, AI Consulting Services
November 1, 2022 – January 1, 2025
Ardigen
Data Scientist III, Microbiome Research
June 1, 2020 – November 1, 2022
Ardigen
Data Scientist, Microbiome Research
May 1, 2018 – May 1, 2020
VoicePIN.com
Machine Learning R&D engineer
August 1, 2016 – March 1, 2018
Cracow Metropolitan Area
VoicePIN.com
Software Engineer
July 1, 2014 – August 1, 2016
Cracow Metropolitan Area
SOFOR Sp. Z O.O.
Software Engineer
October 1, 2013 – July 1, 2014
Cracow Metropolitan Area
SoInteractive
Junior Java Developer
June 1, 2013 – October 1, 2013
Cracow Metropolitan Area
Research on Deep Learning approaches applied to Speaker Recognition problem
December 1, 2014 – February 1, 2016
Role played: Independent researcher consulting with academics Goal: The project aimed to research literature, along with implementation and testing of found solutions, to create a basic voice verification system based on Deep Neural Networks. This would allow easy incorporation of Deep Learning technologies into the main VoicePIN.com products. Actions taken & results: ► Extensive literature research was conducted. ► Many solutions proposed by literature were implemented using Theano or Tensorflow libraries (after its first release in Nov 2015), based on following concepts: » Restricted Boltzmann Machines & Deep Belief Networks. » Autoencoders. » Fully connected feedforward Networks. » Bottleneck architectures (inspired by the speech recognition field). ► The final solution was built around the concept of deep audio embedding into a speaker subspace. The system has achieved accuracy close to the state of the art speaker recognition methods but has not surpassed them. ► At that time (Feb 2016), the project was not taken further due to the insufficient amount of training data and computing resources needed for further steps and the lack of confirmation in the literature of the superiority of deep learning over classical methods. ► The project was revived in 2017, further developed and incorporated to VoicePIN R&D toolbox. ► The project gave me an opportunity to gain a lot of experience and intuition in Deep Learning and by elaborating its theoretical part I have obtained a Master's degree. Tools: Standard Python data analysis stack (numpy, scipy, pandas, sklearn, matplotlib etc.), deep learning libs (theano, tensorflow)
WITKOM (Virtual Sign Language Translator)
July 1, 2014 – December 1, 2014
Role played: a Software Developer Responsibilities: ► Developing a service capable of translating phonetic transcription of sign language (specifically Hamburg Sign Language Notation System) into gestures performed by an Avatar. The service was part of a larger system, which other project members worked on. ► Continuous literature & tools research. ► Reporting research results. Project description: "WITKOM (Virtual Sign Language Translator) is a research and development project carried out by the scientific consortium VoicePIN.com Sp. z o.o. and AGH University of Science and Technology in Krakow in 2013-2015. The project is co-financed by the National Center for Research and Development as part of the Applied Research Program. WITKOM aims to explore the application of image recognition algorithms, machine learning and natural language processing in the context of processing of speech in Polish Sign Language. Solutions developed within the framework of the project can be used to create and improve the components of automatic sign language translation systems into Polish and vice versa. The project is based on an interdisciplinary team composed of electronics, IT specialists, people involved in image processing and artificial intelligence. The team includes specialists in the field of sign language and computer linguistics. As part of the research, we also cooperate with potential users in the future of systems - Deaf."
AWS Partner: Accreditation (Technical)
Amazon Web Services (AWS)
June 24, 2026 – Present
Professional Scrum Product Owner I
Scrum.org
June 24, 2026 – Present
Programming with Python for Data Science
edX
June 24, 2026 – Present
Discrete Time Signals and Systems, Part 2: Frequency Domain
edX
June 24, 2026 – Present
Professional Scrum Master I
Scrum.org
June 24, 2026 – Present
AI for Medical Prognosis
Coursera
June 24, 2026 – Present
Introduction to Linux
edX
June 24, 2026 – Present
Introduction to Probability - The Science of Uncertainty
edX
June 24, 2026 – Present
Ochrona Własności Intelektualnej w AGH
AGH University of Krakow
June 24, 2026 – Present
Introduction to Python for Data Science
edX
June 24, 2026 – Present
Discrete Time Signals and Systems, Part 1: Time Domain
edX
June 24, 2026 – Present
Neuronal Dynamics - Computational Neuroscience
edX
June 24, 2026 – Present
Introduction to Computer Science and Programming
edX
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong inclination towards research and development, particularly in cutting-edge AI/ML domains. Their academic pursuits (PhD) combined with industry roles in companies like Ardigen and VoicePIN.com suggest a preference for environments that foster innovation and deep technical challenges. The diversity of projects, from sign language translation to speaker recognition and microbiome research, indicates a broad interest in applying ML across different fields. The progression into Lead Data Scientist and Product Owner roles also suggests a desire for impact and ownership, which aligns well with dynamic, product-focused teams.
Soft Skills & Operational Fit
The candidate's experience as a Scrum Master and Product Owner, along with leading technical teams, indicates strong organizational, leadership, and communication skills. Their involvement in interdisciplinary research teams suggests good collaboration and adaptability. The detailed project descriptions highlight a methodical approach to problem-solving and a commitment to continuous learning.