Built a classification/recommendations system using Python to predict customers' likelihood of defaulting on payments based on historical contact and payment data. Implemented data processing and querying on Databricks with PySpark and SQL to uncover and communicate actionable insights. Prototyped and deployed text-based LLM models to automate customer service operations. Optimized models through feature engineering and hyperparameter tuning. Built machine learning pipelines supporting time-series forecasting within the data science team. Helped develop customizable anomaly-detection models to identify irregularities in sales across nationwide markets. Contributed to brainstorming and prototyping time-series decomposition frameworks for more accurate forecasts. Created interactive Databricks visualizations using PySpark to communicate findings and metrics.

Data Scientist at The Marketing Store

August 1, 2022 - August 1, 2022

Led data-driven marketing analytics, building predictive models and dashboards to optimize campaigns and improve customer acquisition and retention. Performed exploratory data analysis, engineered features, and implemented pipelines to transform raw data into actionable insights. Collaborated with marketing and product teams to translate analytics into strategy, and delivered reports to stakeholders to inform decision-making.

Machine Learning Intern at Zoom

August 1, 2020 - August 1, 2020

Assisted in building machine learning prototypes and data pipelines during the internship. Loaded and preprocessed datasets, supported model training and evaluation, and contributed to deploying end-to-end workflows.