Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 3

The main achievement of my last work was that I pitched an idea for the portfolio allocation trading framework and prepared a running code for it. The result is that a couple of specific realizations of that framework got fundings and have been running live for almost two years now.

I led the development of the ML model, which takes an individual trading strategy as input and assesses its risk profile. Based on this profile, you can tweak a decision of how much funds to put into that strategy.
Data Scientist at American quant fund, startup (2020-2022, 2.5 years)
The central theme of similar funds is to manage partners' money by investing them across financial instruments. The decision to invest or not is made based on different mathematical, financial, and ML models.

Areas/task examples:
1. ML based predictions for stock/commodities markets. I was responsible for the process of designing and building a predictive model: defining objectives, data preparation, feature engineering, feature importance, model training, optimal params search, comparing results across ML models, sharing thoughts and recommendations on the final results.
2. Implemented my own backtesting simulator in python, statistical framework for evaluating a trading strategy, explainability and visualization utils for it.
3. I was responsible for the portfolio allocation framework, its development, improvement and exploitation of new trading ideas which utilize it.
4. Research and implement ideas from scientific publications to improve existing systems.
5. Moving data around. It included writing SQL queries, processing data with pandas, aggregating data from several sources, writing sampling utils to get structured data for later
modeling.
6. Continuously communicating my findings in technical and non-technical

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Data Science Intern at outsource company in Lviv (2019-2020, 1 year)

Products similarity project. The main goal was to build a ranking products system based on a notion of similarity. That similarity defined in terms of various product features (textual, numerical and images), and the final measure is a composed score of those features.
My responsibilities were: provide ideas of how the final model should work, implement them (functions that get scores from images/numerical attributes + main functions which process all the information and create rankings).

Real-time human-machine interactions project. The main idea was to create a pipeline that takes a video stream as input and detects/recognize motions, gestures and faces.
My responsibilities were: research for existing models, create several parts of the pipeline (using pretrained models via transfer learning + I trained gesture recognition classifier), and code the final model's logic (logic of when, how and which individual parts should be activated).

I was responsible for making final presentations and case studies for both projects.
I worked a lot with ML in finance and would be happy to continue, but I am also open to proposals in other areas. I am deeply interested in various kinds of optimization problems. I believe that the data-driven approach can make a huge difference for businesses. I search for a company that values that too.

I am looking for a company that doesn't cooperate with Russians in any form.