Position: Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 3


ANOMALY DETECTION SYSTEM FOR EV CHARGING STATIONS

Built the anomaly detection system to prevent EV charging station failures.
 
Main responsibilities
Data understanding, acquisition of domain knowledge.
Data processing and data visualization.
Generated features for analyzing ev chargers data and modeling. 
Developing unsupervised models.
Analyzing models performance.
Building dashboards to represent the results.

Tools and technologies used:
Languages: Python.
Frameworks: Scikit-learn, NumPy, Seaborn, Matplotlib
Algorithms: PCA, t-SNE Isolation Forest, k-Means, k-Prototypes, DBSCAN, GMM.
Database: MongoDB.

IMPROVING INSURANCE AGENTS’  PHONE NUMBER PERFORMANCE

Built the system to analyze phone number performance and suggest how to use this phone number to achieve the highest contact rate 
 
Main responsibilities
Data understanding, acquisition of domain knowledge.
Data processing and data visualization.
Hypothesis testing 
Statistical modeling
Presenting results in business oriented manner

Tools and technologies used:
Languages: Python.
Frameworks: Scikit-learn, NumPy, Matplotlib, SciPy
Database: Snowflake.

HR TOOL TO MAKE RECOMMENDATIONS FOR USERS

Built the HR tool to make recommendations for users.

 
My main responsibilities were:
Data understanding.
Collecting and processing raw data from the MongoDB. 
Feature engineering.
Developing recommendation engine.
Developing lambda functions.
Analyzing models performance.
 
Tools and technologies used:
Languages: Python.
Libraries: Scikit-learn, SciPy, Seaborn.
Database: MongoDB.
Cloud Services: Google Cloud Platform.