My name is Stanislav Khrapov. I am a full stack Machine Learning engineer with many years of experience building data science applications starting from data ingestion, modelling, and finishing with cloud deployment and monitoring services. I am passionate about data, automation, code quality, visualisation, and communicating with both technical and non-technical stakeholders.
Currently, I am a senior data scientist at the FinTech startup Chintai based in Frankfurt am Main, Germany. My main project is the development of trade surveillance system based on unsupervised time series classification machine learning models. In the absence of comprehensive training data we have built realistic exchange market simulation with heterogeneous traders. One part of it is the fast Python-based order book matching engine (OrderBookMatchingEngine). To tie all pieces together we have designed a fully automated and reproducible pipeline to simulate market data, train and evaluate ML models catching illegal trading behaviour. As in any startup I also did a little bit of everything IT-related. This includes building GitHub actions based CI/CD pipelines to release Node.js web applications interacting with the blockchain, check code quality, unit and integration tests, search for code vulnerabilities, deploy to Kubernetes cluster running in the cloud. I have also organized processes across the company to streamline and speed up development and release activities starting from a PR and finishing with deployment to the production environment.
In the past, I was a Data Scientist at DB Schenker also based in Frankfurt. There I designed new time series models for forecasting of market freight prices and volumes, company internal financial indicators (EBIT, revenue, receivables, payables, etc.). My responsibilities also included writing end-to-end data ingestion, processing, forecasting, and delivery software mainly in Python and using such tools as web scraping, SQL, pandas, scikit, GitLab, Docker, AWS, Azure, Airflow, etc. I performed research on model comparison in terms of forecasting performance. On top of that I love doing sophisticated visualizations (Seaborn, Dash) for presentation to internal business clients. Most of the time I worked in small teams in agile environment. Finally, I regularly worked as an instructor for the internal AI Training Workshop.
Even before that, as I was working as Assistant Professor of Finance at the New Economic School in Moscow, Russia my area of specialization was financial econometrics, option pricing, volatility modeling. During graduate education and work experience that includes both academic and industry positions I wrote research papers individually and in collaboration. Research topics include but not limited to estimation of multivariate volatility and density models, evaluation of option pricing models, term structure of volatility risk premium, volatility forecasting.
On the personal level I run and swim a lot, bike occasionally (no triathlon, please!). You can join me in this passion on Strava.