Responsible for the entire machine learning development cycle, focusing on time series prediction models. Leveraged approaches such as ARIMA, Prophet, and Recurrent Neural Networks (RNN) including LSTM. Conducted detailed exploratory data analysis (EDA) on real-world datasets, identifying patterns and trends in large datasets to improve model performance. Developed and managed machine learning pipelines using Amazon SageMaker, from model training to deployment. Automated the ingestion, processing, and modeling of data, ensuring efficiency in the workflow and in the deployment of new predictive models to production environments.

Developed automated Python workflows to enhance data collection, cleaning, and transformation processes. Conducted in-depth exploratory data analysis (EDA) to uncover actionable insights. Created and optimized RESTful APIs with Flask and Django. Applied machine learning techniques to analyze aerial imagery and implemented supervised learning algorithms for species classification and growth pattern recognition. Developed robotic process automation (RPA) solutions to streamline business operations.

Led comprehensive exploratory data analysis (EDA) using Python and Pandas. Developed interactive and visually compelling dashboards with Matplotlib, Seaborn, and Dash. Collaborated closely with data engineering teams to design and optimize ETL pipelines. Applied advanced machine learning techniques using Scikit-learn and XGBoost to develop predictive models for credit risk analysis. Designed Python-based data models and algorithms to support investment analysis and portfolio management. Utilized Tableau to create dynamic, high-level executive dashboards. Developed and implemented RESTful APIs. Engineered machine learning models for quantitative trading strategies. Automated routine data processing tasks with Python. Monitored and analyzed market data to identify new investment opportunities.