Simron Waskar

Spearheaded a team of 5 in designing and analyzing A/B tests to evaluate the effectiveness of marketing. Performed advanced statistical analysis techniques in Python, including hypothesis testing, regression analysis, and time series forecasting. Created dashboards using Tableau to visualize key performance metrics, resulting in a 15% rise in efficiency. Customized advanced Excel functions, including VLOOKUP and pivot tables, which led to 20% decrease in manual data manipulation efforts.

Modified 850 MB of the FactSet dataset to construct machine learning models such as regression, decision trees, KNN and SVM to review unstructured content and spot issues or trends impacting financial markets. Revamped cloud-native DevOps architecture and utilized integration tools such as Docker. Developed Python scripts to automate data cleaning and preprocessing, reducing processing time by 42%. Resolved production issues, minimizing downtime by 28% and optimizing data workflows by 35%.

Extracted 90k tweets to analyze public response to water fluoridation in the United States. Built deep learning models like RNN, LSTM and CNN with and without GloVe methods to accomplish sentiment analysis to find correlation between anti-fluoridation and anti-vaccination sentiments.

Managed data storage and processing on AWS, mitigating costs by 20% through resource optimization. Implemented data warehouse solutions to boost performance, reducing query response time by 36%. Established ETL mappings using SQL to extract the company’s unstructured data and transferred it to the staging area to conduct data cleaning by designing star schema data model on Power BI. Generated complex SQL queries within Snowflake, enhancing overall data retrieval efficiency. Evaluated data pipelines and identified performance issues, improving system reliability by 30%. Collaborated with finance and marketing teams to maintain data accuracy and integrity.