Position: Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 0

12th place of my team in the DS NGWL Hackathon.
Passionate machine learning enthusiast and data-driven analyst with the ability to apply
ML techniques and leverage algorithms to solve real-world problems. Gained knowledge
and some experience on Khan Academy, Coursera, Kaggle, Udacity. Have Political Sciences background (have a PhD).

Studied Maths (Algebra, Linear Algebra, Intro to Statistics and Probability, Calculus) on Khan Academy from March to July (2020);
ML and DL on Coursera, Google, Udacity, Kaggle (July - October, 2020), SQL in November, Computer Vision in progress.

Projects:
NGWL.Hack predict churn problem (McKinsey&Company):
Collaborated to the team on competition by adjusting the model development from 69% to 75,5%. Handled missing values and tuned the algorithm, reaching top 12 teams on the
leader-board.
Time Series Analysis projects (Britannia on 'NIFTY-50 Stock Market Data' and Visa on 'New York Stock Exchange Data'): implemented modern time series prediction techniques such as Facebook's Prophet and Neural Prophet, LSTM.

Breast Cancer Image Classification: trained classical ML algorithms and improved the CNN model for getting 81% accuracy on validation data having a small data set.
EDA and Data Visualizations on 'Suicide Rates Overview 1985 to 2016' and 'Summer Olympics Medals (1976-2008)'.
Tinkoff Hackathon task 'Who will like the story?': successfully applied NLP techniques and Feature Engineering that demonstrated the highest (0.44) score on the task metric.
Titanic: achieved 0,80861 model accuracy that gained top 3% on Kaggle leader-board.
Housing Prices Competition for Kaggle Learn Users: reached top 4% on Kaggle leader-board using Regression Analysis.

My strengths: analytical thinking, hardworking, meticulousness, thirst for knowledge, communication.