Strong foundation in regression, classification, clustering, and time series analysis to solve diverse business challenges.
Skilled in designing and training CNNs, RNNs, and transformer-based architectures for advanced machine learning projects.
Expertise in developing NLP solutions using BERT, BERTweet, and related models for various language understanding tasks.
Proven ability to create predictive models for energy market forecasting and anomaly detection, delivering actionable insights.
Proficient in Tableau, Plotly Dash, Seaborn, and Matplotlib to create clear and impactful visual narratives that support decision-making.
Extensive experience in building and optimizing ETL pipelines using Databricks, PySpark, and Airflow for efficient data processing.
Hands-on experience with big data tools to handle large-scale datasets for analytics and modeling.
Successfully deployed ML models using AWS tools such as SageMaker and Lambda for robust real-time applications.
Applied statistical techniques to validate models and ensure data-driven decision-making, including using Bayesian methods.
Skilled in leveraging AWS and GCP for machine learning, data engineering, and model deployment.
Expertise in transforming raw data into meaningful features that enhance model performance and accuracy.
Ensured data accuracy through rigorous validation processes across multiple environments (legacy and Databricks).
