Data Scientist
Current Perform data manipulation using Python (Numpy, Pandas, etc.) to collect, clean, and organize raw data and exploratory data analysis (EDA) applying statistical methods and techniques to extract meaningful insights in Jupyter Notebook. Create and develop data visualizations, interactive reports and dashboards using Excel, Google Sheets, Tableau, Power BI, and various Python libraries; Matplotlib, Plotly, Seaborn, Folium, and Bokeh then present in team meetings and explain findings. Use PostgreSQL to add, update, and query databases and monitor database performance and suggest optimization improvements. Design and implement various machine learning models, including supervised learning (classification and regression) and unsupervised learning (clustering and dimensionality reduction). Conduct image pre-processing tasks such as resizing, normalization, and augmentation using libraries like Pillow, Scikit-Image, and OpenCV to prepare datasets for deep learning models and improve training efficiency. Build, optimize, and evaluate neural network architectures such as ANN, CNNs, Computer Vision, RNNs, LSTM, GRU, and transformers for tabular data regression/classification, image classification/object detection, time series prediction, and sequence modeling processing tasks, Perform data labeling and annotation for image and object detection tasks, utilizing platforms like makesense.ai, roboflow.com, and colab, Utilize NLP(Word2Vec, Glove, Bert, and other models using simple transformers) techniques to preprocess, analyze, and extract insights from unstructured text data, enhancing predictive models and improving decision-making processes. Deploy data science models and interactive dashboards through Flask and Streamlit, streamlining model accessibility and enhancing user interaction with data insights. Familiar with AWS (EC2, RDS, S3, Redshift, and Sagemaker) to build, train, and deploy ML models at scale.
