Economist and data scientist with a dual background that is genuinely rare: a rigorous quantitative degree from HSE ICEF — one of Russia's most demanding economics programmes — combined with hands-on machine learning engineering experience developed through academic research, professional courses, and competitive hackathons.
On the finance side, my track record spans financial modelling at a construction firm, real-estate advisory at KEPT, and transactional accounting at a federal government institution. On the technical side, I have applied ML to crypto markets and real-estate pricing, completed Karpov.Courses' intensive ML programme, and am currently pursuing an MSc in Data Science (Machine Learning Engineering track) at HSE — GPA 4.99.
I am equally comfortable writing production Python pipelines and presenting results to a non-technical audience. My goal is to work at the intersection of quantitative finance and data science — building models that translate into real economic decisions.
Experience
Sep 2024 – present
Presidential Affairs Directorate of Russia — FSBI
Senior Accountant · Banking Operations Group
Responsible for bank account operations within the central accounting department. Day-to-day transactional accounting, reconciliation, and financial reporting for a federal government body.
Aug 2023
KEPT — Real Estate Dept.
Intern
Internship in the real estate advisory practice of a leading professional services firm. Gained hands-on exposure to property valuation methodologies and deal structuring.
2021–2023
OOO SeverStroyTorg
Economist
Economic analysis, financial modelling, and reporting for a construction and trade company. Contributed to budget planning and cost control processes.
Education
2020
IB Certificate Programme
Subjects: English Language, German Language, History
GPA 5 / 5
2020 – 2025
HSE — Higher School of Economics
International College of Economics and Finance (ICEF) · BSc Mathematics & Economics
GPA 4.01 / 5
2022 · "Machine Learning on Crypto Markets" — 8/10
2023 · "The Influence of Social Networks on Price Level: ML Techniques" — 5/10