Азат Гумеров - Data Analyst
Навыки
- AB tests
- Catboost
- Git
- JupyterLab
- ml
- Numpy
- Pandas
- Python
- Scikit-learn
- scipy
- SQL
- Statistics
- XGBoost
Опыт работы
ООО "Цифровая платформа КАМАЗ"
Data Analyst - 08.2022 - 11.20251. Actively participated in the project to create a driver skills assessment system that affects vehicle fuel consumption and service interval. Participation in the project included:
— Working with data: uploading data via Hadoop (using Spark), ClickHouse (widely used SQL queries), S3 and analysis using Pandas, NumPy, Matplotlib, Seaborn, Plotly, SciPy
— Creation of methods for assessing the driving skills of mainline tractor drivers and their verification in cooperation with technical specialists
— Creation of library modules for calculating drivers' driving skills in Python
2. Participated in a project to create smart insurance claims sorting: industrial engineering was used, tools for working with text information (transformers, pymystem3).
3. Created a method for determining the mass of a truck through acceleration using machine learning (sklearn, catboost, lightgbm, shap, phik).НОЦ "ГПН-Политех"
Разработчик Python, Data Analyst - 02.2021 - 02.2022I worked in the project team to optimize the use of mining assets in conditions of various restrictions with the greatest efficiency.
Responsibilities:
1. Writing scripts using calculation methods in Python using pandas, numpy, matplotlib, seaborn, plotly, scipy libraries,
2. Embedding scripts in the service, followed by testing using pytest
3. Participated in the creation of methods for predicting oil production.
Progress:
1. Created a script for calculating liquid production (water + oil) – improves the forecast for oil production
Образование
Проектирование, сооружение и эксплуатация газонефтепроводов и газонефтехранилищ
Уфимский государственный нефтяной технический университет09.2000 - 06.2005
Обо мне
I have worked in engineering positions for many years: there were enough numbers – it took care and the ability to discern the processes behind them. I like to organize information and get far-reaching conclusions.
Working with large amounts of numbers required approaches other than Excel. You can implement such ideas through programming. I tried myself in JavaScript, then Python – I liked its conciseness. I came to data analytics through Python. I've been working with Python in conjunction with Excel for the last few years. I continue to realize myself on this path.
I am ready to move and to work in the office, although I allow a remote or hybrid schedule. He deepened his knowledge of machine learning through a course on Data Science and the application of ML-learning in work.
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