Data Scientist and Developer
Yaroslav is a full-stack data scientist with experience in business analysis, predictive modeling, data visualization, data orchestration, and deployment. He leverages a wide range of machine learning methods, statistics, and business insights to find just the right solution for a problem. Above everything else, he aims to deliver a project that would be truly useful for his clients.
ExperiencePython - 7 yearsTime Series Analysis - 5 yearsStatistics - 5 yearsMachine Learning - 5 yearsData Engineering - 4 yearsSQL - 4 yearsData Visualization - 3 yearsStakeholder Engagement - 3 years
Git, Jupyter, PyCharm, MacOS, Linux, Visual Studio Code (VS Code)
The most amazing...
...thing I've developed is an algorithmic trading strategy powered by multiple data pipelines and one ML model running 24/7.
Algorithmic Trading — Principal Researcher and Developer
- Onboarded dozens of data sources from files, REST APIs, and messaging protocols to a PostgreSQL database. Configured data transformations in the database to create and update features in real time.
- Configured monitoring and alerting systems for data injection using Python and Grafana.
- Analyzed the price and industry data to generate a signal for a high-frequency algorithmic trading strategy.
- Optimized the strategy to maximize P&L while keeping the default risk minimal. Analyzed the L2 price data to estimate the market impact.
- Managed a team of three developers and handled the overall project management.
Developer and Analyst for a Quantitative Research Project
- Analyzed and unified multiple datasets for US equity markets.
- Developed an ML model and several data pipelines of an algorithmic trading strategy.
- Wrote and reviewed both research notebooks and production code.
- Organized a seven-day company meetup, which helped boost team productivity and collaboration.
Energy Trading — Data Scientist
- Created market analysis tools and systematic strategies for coal, power, and crude desks. Covered all phases of a data science project, including project setup, data pipelines, modeling, and deployment.
- Analyzed the firm-wide trading market impact under different execution styles.
- Worked with both small (50 data points) and large (several terabytes) datasets.
- Contributed individually and in collaboration with the data science and IT teams.
- Assisted Vitol's employees in Python and machine learning training.
Model Validation, Commodities — Associate
- Implemented from scratch a custom version of the extended Kalman filter to calibrate exotic option pricing models that outperformed the existing calibration methods.
- Reviewed ten pricing models' options and their implementations in commodities and credit.
- Measured and mitigated numerous model risks in collaboration with the desk and developers.
- Mentored junior employees during their review work.
Algorithmic Trading — Intern
- Designed and implemented two mid-frequency trading strategies for the commodity desk.
- Analyzed portfolio hedging strategies using risk factors for the equity desk.
- Implemented a data pipeline that cleaned and transformed tabular data for the equity desk.
Novosibirsk State University
- Wrote a research paper describing a metric that uses Fourier descriptors to compare shapes with internal gaps.
- Implemented a classification algorithm that achieved 98% accuracy on a dataset with 19 classes of images.
- Presented the results at the scientific conference MNSK 2015, Novosibirsk.
Yet another XML Parserhttps://github.com/mysterious-ben/xmlrecords
Top 1 in Time Series Forecast Competition on Kagglehttps://www.kaggle.com/myster/eda-prophet-winning-solution-3-0
It was very fun to explore and visualize the dataset, to find interesting quirks in it. In particular, soon it became clear that this data had been synthetically generated, which gave out an important clue on how to solve this problem. And it was very exciting that in the end, my analysis paid off and I scored the first place!
Also, I was working on this project with my ex-colleague, so it was a good collaborative experience with just a touch of project management. Of course, it was far from the complexity of managing a real data science project—still, it gave me at least some sense of what might be waiting ahead.
Data Pipelining Toolshttps://github.com/mysterious-ben/apipe
• Lazy computation and cache loading
• Pickle and parquet serialization
• Support for hashing of NumPy arrays and pandas DataFrames
Embeddings in Machine Learning: Making Complex Data Simple
Python, SQL, R, C++, Java, HTML, CSS, XML
Scikit-learn, Pandas, Matplotlib, OpenCV, REST APIs, SQLAlchemy, SciPy, Python Asyncio, Dask, PyTorch, TensorFlow
Jupyter, Git, StatsModels, PyCharm, Amazon Athena, ActiveBatch, MATLAB, Kibana, Plotly, Boto 3, Ansible, GitHub, Bitbucket, Grafana
Data Science, Object-oriented Programming (OOP), Agile Software Development, Functional Analysis, STOMP
Predictive Modeling, Forecasting, Data Analysis, Predictive Analytics, Statistics, Machine Learning, Supervised Learning, Regression, Data Analytics, Time Series, Artificial Intelligence (AI), Time Series Analysis, Mathematics, Data Visualization, Stakeholder Engagement, Data Engineering, Option Pricing, Unsupervised Learning, Finance, Financial Data, Quantitative Analysis, Quantitative Risk Analysis, Statistical Analysis, Web Dashboards, Machine Learning Operations (MLOps), Code Deployment, Algorithms, Futures & Options, Energy, Systematic Trading, Deep Learning, Probability Theory, Mathematical Analysis, Applied Mathematics, Derivative Pricing, Chemistry, Stochastic Modeling, Stochastic Differential Equations, Econometrics, Economics, Computer Vision, Software Development, Genetic Algorithms, Dash, Trading, Financial Markets, Data Mining, Algorithmic Trading, Equity Market Data, Cloud Services, Remote Team Leadership, Technical Hiring, Code Review, IT Project Management, Team Leadership, Dashboards, Quantitative Modeling, Quantitative Finance, Big Data, APIs
LightGBM, Spark, Flask
Jupyter Notebook, Docker, Linux, MacOS, Amazon Web Services (AWS), Visual Studio Code (VS Code)
Oracle SQL, Amazon S3 (AWS S3), SQLite, PostgreSQL
Master's Degree in Financial Mathematics
Université Pierre et Marie Curie - Paris, France
Master's Degree in Applied Mathematics
École Polytechnique - Paris, France
Master's Degree in Mathematics and Computer Science
Novosibirsk State University - Novosibirsk, Russia
Bachelor's Degree in Probability and Statistics
Novosibirsk State University - Novosibirsk, Russia