M.S. in Business Analytics graduate with 3+ years of experience, skilled in solving diverse business problems, analyzing and visualizing data, developing predictive models, applying statistics, and advanced analytical techniques to facilitate data-driven decision making. I see myself leveraging data, critical thinking, analytics, and business acumen to find actionable insights and facilitate data-driven decision making.
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Experience
As an Analytics Consultant at Carlson Analytics Lab, I have worked with 4 US-based clients on analytics-driven business solutions. Recently, I worked as an Analytical Lead with a large investor to deliver evaluation metrics and feature importance. Primary tools used were Python and R. I developed a transfer learning system through machine learning on historical data of 1800 funded projects and 100+ evaluation metrics to help evaluators make informed decisions for driving future projects to success. I identified feature importance through statistical analysis, Principal Component Analysis, regularization algorithms (Elastic Net, ordered Logit) and tree-based models (XGBoost) of scikit-learn.
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Projects
I presented a project on Big Data Analytics using AWS at the trends marketplace event at school. I had designed an end-to-end inventory management framework on AWS using S3, SageMaker(ML), Python, SQL, and a QuickSight dashboard to predict sales and track inventory KPIs. This use case showcased the cloud processing and storage capabilities of AWS to analyze and manage demand/sales and inventory.