Stefan Petrov
Verified Expert in Engineering
Machine Learning Developer
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.
Portfolio
Experience
Availability
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.
Work Experience
Machine Learning Engineer
Freelance
- Developed machine learning algorithms for stock price prediction, resulting in Sharpe ratios of 3.2. Used an ensemble of ML, neural net, and traditional forecasting mechanisms. Created and developed an API and data pipeline using Google Cloud.
- Worked on the analysis of news, processed through an NLP pipeline, to FOREX rate change predictions.
- Did prediction and optimization of required inventory levels for inventory stock levels with many item types (>30000 SKU). Used ML classification and probabilistic programming, combined with operational research techniques.
- Estimated type and severity of mental diseases from self-reported surveys (using NLP and ML techniques). Collaborated with a specializing doctor of medicine (psychiatry).
- Worked on Spatio-temporal analysis of demographic data for income estimation on the fine-grained spatial scale (scale was different for different countries). Used particular Spatio-temporal statistical techniques.
Head of Research
Transmetrics
- Worked on one-to-three projects as the key analytical contributor on all matters analytical matters (e.g. anything requiring Optimization, statistical, ML, or AI ). projects.
- Used ML, Statistical, and Deep Neural Network techniques for forecasting of demand time series.
- Modeled and optimized problems transportation companies have, like: routing, network design, warehouse optimization, using ML, Linear Optimization, Approximation Algorithms MIP, CP, and Reinforcement Learning, and Probabilistic Programming.
- Analyzed and negotiated business requirements and project directions, considering:. •data availability,. •business requirements,. •feasibility and technology to solve certain types of problems,. •performed a counterfactual analysis to prove value.
- Businesses-collected operational data very well, but sometimes it didn't support analytical tasks so well (e.g., important data is only partially collected). Used ML, probabilistic programming, and convex optimization on such problems.
- Performed code reviews, directed and facilitated the problem-solving process, read papers and disseminated knowledge throughout the org.
- Worked on estimated time-of-return of rented assets, using survival analysis and other ML/statistical techniques.
- Hired, interviewed, and mentored team members. Participated in product development and pre-sales.
Quantitative Developer
CommEq Asset Management
- Developed an algorithmic trading system with Python. Refactored the codebase to use six times fewer Lines of Code. Changed up the Machine Learning algorithm in question with one, performing better on the backtests.
- Performed some natural language processing work for news classification and tagging (multi-label classification problem), significantly improving on the currently used models. The measurement was Hamming-Loss.
- Worked on portfolio rebalancing part using Convex Optimization techniques.
Quantitative Analyzer
Bwin.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). Used ML and Statistical techniques, e.g. XGBoost classification, etc.
- Revamped volleyball and a couple of other point sports models, boosting the P&L on them significantly. Used Numerical analysis algorithms, backtesting, and ML. Participated in basketball and tennis models, too.
- Developed large parts of the currently used customer classification (dangerous or not, VIP or not, about to churn/not, and so on) models. Used Machine Learning, Deep Learning, and Probabilistic Programming Algorithms.
- Handled pricing problems (pricing combos). Used Approximation algorithms for a nonlinear optimization problem for this.
- Developed sports models libraries (directly deployed in production) and simple GUI clients (for Quality control by the traders) with F# and Mathematica.
Quantitative Developer
Blue Edge Bulgaria | Evolution Capital Management
- Developed high-frequency trading strategies, aimed at trading in Japan, using specialized versions of PCA, ML, and optimization.
- 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).
Quantitative and Back-end Developer
Cayetano 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.
Experience
High-frequency Backtesting Engine
Skills
Languages
Python, Python 3, R, SQL, F#, Wolfram, Julia, C#, Scala, Java
Tools
StatsModels, Mathematica, JetBrains, CPLEX, IBM Watson, Tableau
Paradigms
Data Science, Linear Programming, Constraint Programming, Business Intelligence (BI), Anomaly Detection
Other
Data Analytics, Algorithms, Applied Mathematics, Time Series Analysis, Forecasting, Linear Regression, Data Visualization, Graph Theory, Mathematical Modeling, Time Series, Machine Learning, Data Reporting, Product Analytics, Project Leadership, Data Mining, ARIMA Models, Statistical Modeling, Neural Networks, Natural Language Processing (NLP), Software Development, SaaS, Mathematical Finance, Statistics, Bayesian Statistics, Supply Chain Optimization, Mixed-integer Linear Programming, Gradient Boosted Trees, Customer Segmentation, GPT, Generative Pre-trained Transformers (GPT), Product Design, Spatial Analysis, Reinforcement Learning, Operations Research, Team Leadership, Teamwork, Gradient Boosting, Convex Optimization, Data Analysis, Probability Theory, Numerical Programming, Dashboards, Principal Component Analysis (PCA), Economics, Computer Engineering, Root Cause Analysis, Logistic Regression, Logistics, Deep Neural Networks, Recurrent Neural Networks (RNNs), Random Forests, Support Vector Machines (SVM), Clustering Algorithms, Stochastic Modeling, Hierarchical Clustering, Google Data Studio
Frameworks
RStudio Shiny, Apache Spark, .NET
Libraries/APIs
Scikit-learn, SciPy, TensorFlow, Flask-RESTful, XGBoost
Platforms
Jupyter Notebook, RStudio, Docker, Linux, Ubuntu, Google Cloud SDK
Storage
MongoDB, Teradata, Microsoft SQL Server, PostgreSQL
Education
Master's Degree in Mathematical Modeling in Economics
Sofia University - Sofia, Bulgaria
Bachelor's Degree in Computer Engineering
Nagoya Institute of Technology - Nagoya, Japan
Bachelor's Degree in Applied Mathematics
Sofia University - Sofia, Bulgaria
Certificate in Japanese Language Course
Tokyo University of Foreign Studies - Tokyo, Japan
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