Data Scientist
CurrentJoined an IT, app development, and HR services company when data was unorganized, optimized it for business analysis, and provided critical intelligence for various functions, including sales, marketing, CRM, and finance. Led strategies for data governance and data warehousing, built ETL pipelines, and implemented several machine learning models.• Identified the most infrequent customers and recommended sales-boosting incentives. Segmented customers, preprocessed sales data, employed cohort and RFM analyses to understand customer loyalty tiers, and implemented a sales forecasting model.• Reduced fraudulent credit card activity by helping to implement machine learning models, using a large transaction data set. Preprocessed and balanced the data, and implemented models to maximize accuracy.• Increased a department’s holiday sales by detecting sales trends and forecasting sales using sales data. Utilized forecasting and regression models.• Provided insights about email marketing campaigns by conducting sentiment analysis and creating an efficiency classifier. Performed text preprocessing and vectorized texts for machine learning.• Identified top email marketing campaigns by defining KPIs, tracking them, and building dashboards. Performed ETL transformations, implemented user filters, and provided actionable insights.• Compared a major healthcare client’s ranking among peers and found opportunities for CMS funding by researching hospital funding incentives, identifying top-performing hospitals, and presenting healthcare informatics to stakeholders.• Reduced data preparation time and improved data performance by migrating data from Excel to Power BI, introducing it for both ETL and visualization, and defining metrics to track data changes.• Decreased processing time for model training by using various Python libraries and machine learning models. Employed several metrics, including confusion matrix, accuracy score, F1 score, and R-squared score.
