As a Data Science Intern at Data Glacier, I was responsible for improving the accuracy of machine learning models for the Bank Term Deposit Customer Targeting Project. This was achieved through comprehensive data pre-processing techniques, data imputation using SMOTE, and normalization. In addition, I developed a highly effective G2M strategy for a cab investment company that resulted in a significant increase in revenue and helped identify the most promising cab firms for investment. I also collaborated with senior data scientists to design and implement data pipelines using Python, SQL, and statistical techniques for efficient data processing. Finally, I used the same tools to process, analyze, and create BI dashboards of large datasets.

As a System Engineer I have increased returns by creating impactful dashboards and implementing an ad-hoc client reporting framework. I have designed and implemented robust data pipelines using Java and SQL to automate data processing for over 10 million customer records, as well as process, analyse, and visualise large datasets. I have created a predictive modelling tool from scratch using SQL that analysed customer data and forecast tax projections, enhancing tax planning processes and driving a subsequent 10% revenue growth. I have proactively identified and resolved caching-related issues within existing business logic, leading to a 30% improvement in server efficiency and saving the company in cloud computing costs. I have also demonstrated exceptional collaboration within cross-functional teams, successfully delivering 3 projects on time and driving a substantial 15% increase in revenue.