Head of Research
2016 - PRESENTTransmetrics- Led the data science team by handling daily business tasks, hiring, and mentoring people.
- Participated in product direction development and pre-sales.
- Modeled and optimized various problems transportation companies have, such as: routing, network design, warehouse optimization, and so on.
- Analyzed and negotiated business requirements.
- Performed a counterfactual analysis and simulations for to prove the value to the customer.
Technologies: Docker, TensorFlow, CPLEX, PostgreSQL, Python, RQuantitative Developer
2016 - 2016CommEq Asset Management- Developed an algorithmic trading system with Python.
- Performed some natural language processing work for news classification.
Technologies: R, MongoDB, PythonQuantitative Analyzer
2013 - 2016Bwin.Party Digital Entertainment- Developed modules that determined the probabilities for events of interest (goals, points, and so on) and their prices (odds) for the sporting events, offered on the company sites. Used mostly Mathematica and F#.
- Solved various BI questions (e.g., the impact of a new sports model release on revenue in that sport, customer classification, and others).
- Revamped volleyball and a couple of other point sports models, boosting the P&L on them significantly.
- Developed large parts of the currently used customer classification (dangerous or not, VIP or not, about to churn/not, and so on) models.
- Participated in a secondary role in basketball and tennis models.
- Handled pricing problems (pricing combos).
Technologies: Microsoft SQL Server, Teradata, R, Mathematica, F#Quantitative Developer
2013 - 2013Blue Edge Bulgaria | Evolution Capital Management- Developed high-frequency trading strategies, aimed at trading in Japan.
- Implemented the main approach via technical trading-trying to identify persistent regimes in the market, based on detecting correlated quantities in the electronic trading book.
- Worked on the backtesting/trading platform (C#, MS SQL stack).
- Developed with R/Python for EDA and prototyping and estimation of upper bound of strategy performance before trading complications such as latency and transaction costs are considered.
- Researched about order book dynamics (e.g., the expected lifetime of bids).
Technologies: Microsoft SQL Server, R, Python, C#Quantitative and Back-end Developer
2012 - 2012Cayetano Gaming- Developed a slot game with a game designer who would specify the game logic, what type of bonuses and mini-games existed, and provided the vision of how the game should "feel." This is expressed in terms of volatility of payout, bonus structure, bonus frequency, etc. For example, a "chill" could pay out small amounts very often, while a suspense-filled game might have less frequent larger payouts.
- Worked in a team that was responsible for writing game logic and developing in-house combinatorial optimizer in order to find suitable symbol distribution so that the game "feel" and expected payout properties are satisfied.
- Wrote three games, one of which was novel in that there was some strategy involved on the player side. This required different pricing techniques.
- Implemented an ad-hoc statistical analysis for roulette behavior.
Technologies: Java
Stefan Petrov
Mathematical Models Developer in Sofia, Bulgaria
Member since April 15, 2019
Stefan has been fascinated by the potential of AI since high school. For the past several years, he’s been making a living as the primary developer of more than ten analytical products using R, Python, Mathematica, .NET, and Java. Recently, Stefans been focusing on operations research-type issues and using random forests, time series models, Keras, TensorFlow for neural nets, PyMC3, Stan for Bayesian models, and linear and MIP programming.
Stefan is now available for hire
Portfolio
- TransmetricsDocker, TensorFlow, CPLEX, PostgreSQL, Python, R
- CommEq Asset ManagementR, MongoDB, Python
- Bwin.Party Digital EntertainmentMicrosoft SQL Server, Teradata, R, Mathematica, F#
Availability
Full-time
Preferred Environment
PostgreSQL, JetBrains, RStudio
The most amazing...
...thing I've coded was a successive approximation algorithm for the optimal trucking schedule. We used a combination of column generation and L1 relaxation.
Employment
Experience
- High-frequency Backtesting Engine (Development)
I developed a backtesting engine for semi high-frequency trading (1 minute) in R. The trading system achieved a Sharpe ratio of 1.85 in backtesting. I used R, XGBoost, regularized linear models, and an interface in Shiny.
Skills
Languages
Python, Python 3, R, SQL, F#, Wolfram, Julia, C#, Scala, JavaTools
StatsModels, Mathematica, JetBrains, CPLEXParadigms
Data Science, Linear ProgrammingOther
Data Analytics, Algorithms, Applied Mathematics, Time Series Analysis, Forecasting, Linear Regression, Data Visualization, Mathematical Models, Time Series, Machine Learning, Data Reporting, Product Analytics, Project Leadership, Data Mining, ARIMA Models, Statistical Modelling, Neural Networks, Mathematical Finance, Statistics, Bayesian Statistics, Supply Chain Optimization, Mixed Integer Linear Programming, Gradient Boosted Trees, Customer Segmentation, Product Design, Spatial Analysis, Logistic Regression, Logistics, Deep Neural Networks, Recurrent Neural Networks, Random Forests, Support Vector Machines (SVM), Clustering Algorithms, Stochastic Modeling, Hierarchical ClusteringFrameworks
RStudio Shiny, .NETLibraries/APIs
Sklearn, SciPy, TensorFlowPlatforms
Jupyter Notebook, RStudio, Docker, Linux, Ubuntu, Google Cloud SDKStorage
MongoDB, Teradata, Microsoft SQL Server, PostgreSQL
Education
- Master's degree in Mathematical Modeling in Economics2009 - 2012Sofia University - Sofia, Bulgaria
- Bachelor's degree in Computer Engineering2008 - 2012Nagoya Institute of Technology - Nagoya, Japan
- Bachelor's degree in Applied Mathematics2005 - 2009Sofia University - Sofia, Bulgaria
- Certificate in Japanese Language Course2007 - 2008Tokyo University of Foreign Studies - Tokyo, Japan
