Martin Melicherčík
Lead Data Scientist
Vienna, Austria
Summary
👤
Senior🎓
Top SchoolMartin Melicherčík is a Lead Data Scientist based in Vienna with 11 years of experience turning complex data into actionable business impact across banking and consumer software. He combines a strong mathematical and statistical foundation (MSc in Probability, Mathematical Statistics and Econometrics) with hands-on mastery of end-to-end data engineering, forecasting, A/B testing and ML in production using tools from Hadoop and SQL to Python, R and Tableau. At Erste Group and previously at Avast he led customer data mart development, advanced churn and targeting models, and automated analytics pipelines that directly informed product and revenue decisions. Comfortable operating at the intersection of analytics, engineering and business, he routinely applies Bayesian methods and automated alerting to uncover subtle signals in large-scale datasets. His background in credit portfolio monitoring gives him additional rigor in risk-aware modeling and statistical governance.
11 years of coding experience
6 years of employment as a software developer
Master’s Degree Probability Mathematical Statistics and Econometry, Master’s Degree Probability Mathematical Statistics and Econometry at Charles University
English, Spanish, Czech, Slovak, German
Github Skills (11)
convolutional-neural-networks3
super-resolution3
3d2
deep-learning2
graphql1
mongoose1
typescript1
python1
mongodb1
nodejs1
react1
Programming languages (2)
Github contributions (5)
Compotes/driver_board
Feb 2016 - Apr 2017
Contributions:8 PRs, 13 pushes, 5 branches in 1 year 2 months
ringmotorslave
Meli-0xFF/depthmap_sr
Sep 2022 - Jun 2023
In this master thesis project we work on depth map super resolution using deep learning techniques. Dataset samples are low resolution (LR) depth maps and intensity textures of scanned scene from Photoneo 3D scanner. Ground truth for every sample is high resolution (HR) depth map. Our goal is to design convolutional neural network with an architecture suitable for this problem. We also plan to propose evaluation metric for output depth maps.
Contributions:38 PRs, 39 pushes, 18 branches in 9 months
3dconvolutional-neural-networksdeep-learningsuper-resolution
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