Data Scientist | Analytics and Machine Learning

Python Programming
Advanced Python programming with expertise in data science libraries including Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for data analysis, visualization and machine learning model development.

Machine Learning Engineering
Development of machine learning models including Random Forest, Decision Trees, SVM, LGBM Regressor, and CatBoost for various applications such as predictive analytics and environmental monitoring.

Data Analysis & Visualization
Statistical analysis and data visualization techniques using R, Python, and specialized tools to transform raw data into actionable insights and business intelligence dashboards.

R Programming
R programming and R Studio for statistical analysis, data modeling and visualization, particularly for forecasting applications.

SQL & Database Management
SQL querying and database management skills for data extraction, transformation, and cleaning operations across various data sources.

Data Cleaning & Preprocessing
Expertise in data cleaning, deduplication, standardization of formats, and handling of missing values to ensure data integrity for analysis.

Cloud Services
Working knowledge of cloud services for deploying data science solutions and managing data pipelines.

Version Control
Proficiency with Git and version control systems for collaborative development and code management.