Pakhurov Fedor

Moscow
Mob: +7 (929) 575-77-55
Email: [email protected]
GitHub: github.com/fpkh

Personal Profile Statement

Computer science sophomore, highly motivated to practically apply obtained theoretical knowledge in Advanced Mathematics and Programming at position of Data Scientist/ML Engineer. In my current studies, I participated in projects on model-view-controller database app and credit scoring app; side internship on credit scoring and data collection script.

Skills

• Python (Level 3 Algorithms&Data Structures):
  — numpy, pandas, scikit-learn — active user;
  — xgboost, lgbm, seaborn, matplotlib, plotly — occasional user with documentation;
  — tensorflow keras, pytorch, nltk, bigquery — beginner.

• MacOS zsh, Linux VM bash — daily usage, basic scripts.


• Conda venvs, Docker Containerization — satisfactory for Python projects.

• C — along with Computer Architecture and Operating Systems course:
  — static/dynamic libraries;
  — synchronisation, interprocess communication;
  — users, groups, permissions.

• HTML (basic understanding; mainly used for Python projects).


• C++ (Level 2.5 in ADS I'd say: overall competence and lack of understanding of some language-specific aspects):
  — stdlib;
  — Qt Framework (cross-platform apps).

• Assembly (RISCV-32).

• Markdown — current CV is written in it.

• Russian (native), English (IELTS 7.5 ~ CEFR C1), French (CEFR A1).

Education

2022–2025   BS Data Science and Business Analytics   LSE
Relevant modules:

• Business and Management in Global Context;
• Probability Theory;
• Mathematical Statistics.

2021–2025   BCs Applied Mathematics and Informatics   HSE   (GPA 5.77)
Relevant modules:

• Calculus and Differential Equations;
• Linear Algebra;
• Algorithms and Data Structures;
• Computer Architecture and Operating Systems.

Relevant coursepaper:

      "Program for Assessing the Creditworthiness of Borrowers Based on the Logistic Regression Algorithm":

• scikit-learn data prep + LogReg model;
• Flask web-app;
• Docker container;
• version control via GitHub.

2010-2022   Secondary education   Language Gymnasium   Gold Medal

Internships

August 2022   Summer practice   MobileScoring

   BigData+ML research project:

• Feature selection/engineering methods comparison (scikit-learn);
• Classification model tuning and selection (sklearn.linear/.tree/.ensemble, XGBoost, LightGBM, CatBoost);
• Proposition of company with employment of .parquet file format;
• Rollout of 2 boosting-based on internal PSI platform.

April 2023   Practice internship   MobileScoring

   Data Scraping + Merging with existing database project:

• Python requests+BeautifulSoup4 for html content parsing;
• lxml for parsing xml data;
• Pandas for merging datasets.

Hobbies

Drummer with secondary professional education. Adore kart racing and non-fiction literature. Enjoy dealing with PC hardware. Typing with 60 WPM rate :)