2019-2019

Data Science Intern at Kicktrans Technologies

• Consolidated unstructured text to be analyzed from customer reviews into data frame applying normalization techniques. • Programmed NLP text classification ML models using CART and Random Forest to predict customer product recommendations using data-driven solution. • Co-developed data collection, analysis, and reporting workflow using Power BI, presenting KPIs to senior leadership.

2020-2021

Data Scientist at Schutzen Chemical Group

• Developed data science app on AWS to scrape competitor and demographic data, streamline pricing analysis and decision-making, identify areas of improvement, reducing market research time by 50%. • Created Power BI reports with data modeling for weekly/monthly inventory and sales tracking; used by management team to meet 95% of orders within 7 days. • Implemented end-to-end financial forecasting and regression models using Snowflake ETL and Python to improve ecommerce sales, resulting in 25% revenue increase and 20% cost reduction.

2022

Data Analyst Intern at Learning Tree International

• Deployed NLP architecture for business entity standardization, collaborating with cross-functional teams, minimizing operational challenges and saving over $20k/yr. • Discovered and presented course selection trends of 200K customers using association rules (Python, Apache Airflow ML Pipeline, Power BI) for marketing to maintain 85% customer retention. • Utilized Classification and Collaborative Filtering technique to build and maintain a Course Recommendation Model that predicts the student’s interest in the subject with 99% accuracy and 96% precision.

2022-2022

Data Analyst Intern at Energetiq Technologies

• Optimized financial data integration by 60% using ETL pipelines (SSIS) and ensured data quality for presenting quarterly business insights & KPIs to stakeholders. • Designed MS SQL Server database with engineering team featuring 21 entities, 3 stored procedures, triggers and constraints, improving data QA and reducing calibration time by 3 weeks. • Automated and monitored supply chain and marketing performance tracking, analysis and reporting, saving 8 hrs/week using custom ETL pipeline (SQL, Python, Power BI).