Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 11

I started to code at 21 years old as a complete beginner with a single goal in mind, to program and launch a social network. The website was successful and attracted around 300 people. Three years after that, I assumed a senior position as a frontend developer. In the following years, I mentored and even tutored a dozen people in programming. About one-third of them have found jobs in software development.

At 24 years old, after an unexpected occurrence of playing a Fallout game in English despite knowing none of it, I realized that learning a language might have been easier than I had previously thought. I have started reading books in English voraciously since then, and now I do all my notes and drafts in English.

I started doing math at 27 years old (only 4 years ago). In the first year of studying, I did more than 1600 school-level math problems. Then I completed several advanced math, CS, and ML courses. I’ve been working in ML for two years now.
I've been programming for 10+ years, mostly in Python and TypeScript. I’m pretty knowledgeable about IT in general and have developed deep expertise in several areas. In the last 5 years I have worked with several teams building complex applications. I swiftly absorb new knowledge and often practice maths. Overall I have more than 8 years of experience in software development and 2 years in financial machine learning.

In my current position, I have set up a complex architecture for data collection from crypto exchanges and designed an ML pipeline from the ground up. I've worked with time series and employed exotic metrics (i.e. concordance index). I have compiled a survey of the methods used in financial data analysis.

In my previous job, I predicted stock market fluctuations from public data (news, SEC filings) using classical ML algorithms and Transformer models. I worked with all parts of the process, from collecting data to deploying models.

Before that, when I worked for Waves platform, I was a front-end team lead working on the client application for the Waves blockchain. I designed the architecture and performed security analysis, created a library to work with cryptography and the blockchain API. I also gave lectures about blockchain, including a talk at HighLoad++ in 2017.

Full details of my work experiences are listed on LinkedIn.
I want two things from my future job: 1) contribution to the well-being of society and 2) more structural complexity, in the sense of having a lower noise-to-signal ratio in the data (in finance, the signal is very low and the structure of the data is practically flat — everything revolves around reducing noise and getting what crumbs are left of the signal).

The perfect job for me is 70% deep learning research and 30% spent on its implementation. Despite my lack of experience, I know how to learn quickly and can generate lots of ideas that I can also implement and test.

I certainly do not want to work in entertainment (selfie-improving apps, funny chatbots, etc). I'd rather not work with structure-poor CSV data if that's the only kind of data to work with.