Design and development of supervised and unsupervised machine learning models for classification, regression, and clustering tasks.

Expert in model evaluation and optimization techniques such as cross-validation, grid search, and hyperparameter tuning.

Experience with key ML frameworks and libraries including TensorFlow, and Scikit-learn.

Deploy machine learning models into production using REST APIs, Flask, FastAPI, or cloud platforms.

Build end-to-end ML pipelines integrating data preprocessing, feature engineering, model training, and deployment.

Skilled in real-time and batch inference techniques for scalable machine learning applications.

Expertise in data wrangling, cleaning, and transformation to prepare datasets for analysis and modeling.

Hands-on experience in building machine learning models using Python, scikit-learn and TensorFlow

Strong knowledge of statistical analysis, data visualization, and predictive analytics.

Ability to design and maintain ETL pipelines for processing structured and unstructured data.

Experience working with large datasets and databases such as SQL, NoSQL, and cloud data warehouses (e.g., BigQuery, Redshift).

Proficient in data storytelling using tools like Power BI, Tableau, and Matplotlib.

Design and development of robust data pipelines for extracting, transforming, and loading (ETL) data from diverse sources.

Perform in-depth data analysis to uncover patterns, trends, and actionable insights using Python, SQL, and Excel.

Create interactive dashboards and reports using Tableau, or Looker for effective data storytelling.

Ensure data quality, consistency, and governance across pipelines, storage layers, and analytical outputs.

Deployed scalable data science applications using AWS services like EC2, S3, Lambda, and SageMaker for model training and hosting.

Built automated data pipelines using AWS Glue, Step Functions, and CloudWatch for orchestrating ETL jobs and ML workflows.

Integrated GCP services like BigQuery and Vertex AI for real-time analytics and large-scale model training and deployment.

Implemented model versioning, endpoint monitoring, and CI/CD pipelines for continuous delivery of ML services on cloud platforms.

Optimized cloud resources for cost-efficiency while maintaining high availability and performance of data science workloads.