Machine Learning Engineer2019 - PRESENTFreelance
Technologies: Python, Machine Learning, Statistics, Reinforcement Learning, Natural Language Processing (NLP), Operations Research, Flask-RESTful, R, Julia
- 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 Research2016 - PRESENTTransmetrics
Technologies: Docker, TensorFlow, CPLEX, PostgreSQL, Python, R, Mixed-integer Linear Programming, Constraint Programming, Deep Neural Networks, Machine Learning, Statistics, Bayesian Statistics, Time Series Analysis, Team Leadership, Teamwork, IBM Watson
- 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 Developer2016 - 2016CommEq Asset Management
Technologies: R, MongoDB, Python, Natural Language Processing (NLP), Machine Learning, Time Series Analysis, Gradient Boosting, XGBoost, Convex Optimization
- 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 Analyzer2013 - 2016Bwin.Party Digital Entertainment
Technologies: Microsoft SQL Server, Teradata, R, Mathematica, F#, Tableau, Business Intelligence (BI), Data Science, Data Analysis, Probability Theory, Numerical Programming, Machine Learning, SQL, RStudio Shiny, Dashboards
- 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 Developer2013 - 2013Blue Edge Bulgaria | Evolution Capital Management
Technologies: Microsoft SQL Server, R, Python, C#, Machine Learning, Mathematical Finance, Statistics, Time Series Analysis, Convex Optimization, Principal Component Analysis (PCA)
- 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 Developer2012 - 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.