Volodymyr Holomb
Since 2021, I have been the Lead Data Scientist and ML Operations Engineer at RBC Group. Before this role, I held leadership positions at the regional Chamber of Commerce and Industry for almost a decade.
My career journey has spanned from leading an economic research centre to software development, shaping my pragmatic approach to data science. As a former business consultant, I possess an original skill set that allows me to bridge the gap between business needs and the technical nuances of data analytics.
I began my journey in IT as a self-driven learner and have since earned a formal Master's degree in Software Engineering, which has further enhanced my hands-on experience.
In addition to my professional roles, I have also taught finance and credit courses. My teaching background, along with my diverse experience, drives my passion for sharing practical knowledge.
Education
Postgraduate
2018 – 2023
Master of Science, Software Engineering
Zaporizhzhia National University
Postgraduate
2002 – 2007
Master's Diploma, Interpreting
University of the Humanities "ZIPMA"
Postgraduate
2002 – 2007
Master's Diploma, International Economics
University of the Humanities "ZIPMA"
Experience
Lead Data Scientist & ML Operations Engineer
RBC Group
2021 – Present
Software Developer
Group BWT
2020 – 2021
Head of the Expertise Department
Zaporizhzhya Chamber of Commerce and Industry
2013 – 2020
Head of the Applied Economic Research Centre
Zaporizhzhya Chamber of Commerce and Industry
2010 – 2013
Educator of the Finance and Credit Department
Classic Private University
2009 – 2010
Leading Expert of the Informational Department
Zaporizhzhya Chamber of Commerce and Industry
2007 – 2009
Publications
Precision Clustering Made Simple: kscorer’s Guide to Auto-Selecting Optimal K-means Clusters
Visualizing Data Made Easy with ProSphera
An ImPULSE to Action: A Practical Solution for Positive Unlabelled Classification
A Practical Approach to Evaluating Positive-Unlabeled (PU) Classifiers in Business Analytics
Holidays auto-modelling for efficient time-series forecasting
How do we know that your customers are leaving?
Time series batch processing for outlier detection
Building memory-efficient meta-hybrid recommender engine: back to front (part 2)
Building memory-efficient meta-hybrid recommender engine: back to front (part 1)
Borderless tables detection with deep learning and OpenCV
Side-by-side OCR in Python with Google Vision and Tesseract
Data extraction from a PDF table with semi-structured layout
Українці будуть бідними, поки самі не захочуть цього змінити