Josef Ondrej
Verified Expert in Engineering
Mathematical Modeling Developer
Prague, Czech Republic
Toptal member since November 12, 2019
Josef has a background in mathematics with a specialization in statistics and probability theory. He has five years of experience with applying his skillset to modeling various real-world problems ranging from in-play odds calculations for sports matches to building AI solutions in the field of conversational agents.
Portfolio
Experience
- Mathematical Modeling - 9 years
- Statistics - 7 years
- Python - 5 years
- NumPy - 5 years
- Deep Learning - 3 years
Availability
Preferred Environment
GitHub, PyCharm, Ubuntu, Linux
The most amazing...
...thing I've worked on was a model for calculating in-play odds for tennis and volleyball matches in a major gambling company.
Work Experience
Research Scientist
IBM Watson
- Contributed to research on how dialog agents can be designed using a declarative paradigm and planning technologies like PDDL.
- Implemented a production-grade system that enables dialog agents to automatically improve based on their interactions with the end-users.
Mathematician
Tipsport
- Developed mathematical models for calculating in-play odds for various sports matches including tennis, (beach-)volleyball, badminton and horse racing.
- Scrapped website to collect data for subsequent analysis and model parameters estimation.
- Developed a model for predicting the risk rating of clients based on their previous betting activity.
- Worked on an image recognition model for tracking the position of a ball in table tennis.
Experience
Live Odds Models
Plotting Text in Spectral Analyzer
https://github.com/josefondrej/word2waveMNIST explained
https://github.com/josefondrej/mnistEducation
Master's Degree in Statistics, Probability Theory and Econometrics
Charles University - Prague, Czech Republic
Bachelor's Degree in General Mathematics
Charles University - Prague, Czech Republic
Certifications
Machine Learning
Coursera
Sequence Models
Coursera
Convolutional Neural Networks
Coursera
Structuring Machine Learning Projects
Coursera
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
Neural Networks and Deep Learning
Coursera
Skills
Libraries/APIs
NumPy, Theano, TensorFlow, Keras, Pandas, Matplotlib
Tools
GitHub, PyCharm, IntelliJ IDEA, Jenkins, Hidden Markov Model, ARIMA
Languages
Python, Java, C#, R, SQL, CSS, HTML, Bash
Frameworks
Flask, Selenium
Paradigms
Declarative Programming, Imperative Programming, Agile Software Development, Concurrent Programming
Platforms
Linux, Ubuntu, Docker, Kubernetes
Storage
SQLite, PostgreSQL, MongoDB, JSON
Other
Bayesian Statistics, Bayesian Inference & Modeling, Statistics, Probability Theory, Regression Modeling, Robust Regression, Logistic Regression, Polynomial Regression, Regression, Mathematical Modeling, Markov Chain Monte Carlo (MCMC) Algorithms, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Machine Learning
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