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‣ Analyzed requirements and developed the data models (Conceptual, Logical and Physical), schema designs (Star and Snowflake) and loaded it into databases (Oracle) using Informatica as ETL tool and Erwin as Data Modeler.‣ Designed and developed business intelligence dashboards, analytical reports and data visualizations using Tableau by creating multiple measures using LOD expressions for different user groups, operations and finance team needs.‣ Cleaned unstructured data by processing the data using Python Pandas and NumPy, developed database objects, including tables, views, and materialized views to store data in a structured format using Oracle SQL.

‣ Optimized report generation for various clients by designing personalized parameters and using advanced SQL queries in Oracle BI. Reduced redundancy in the number of reports by 65%.‣ Designed and developed interactive dashboards to help a client in analyzing and assessing their sales incentive compensation, thereby reducing the manual work of calculating sales incentives by 50 hrs./week.‣ Developed yearly compensation plans by speaking with the client’s key sales leaders regarding design, implementation, and also model compensation plan designs against future sales metrics for cost estimations and financial accruals to accomplish the speeding of calculation of the incentives process by 80%.

‣ Performed predictive analysis and anomaly detection to identify fraudulent behavior in KYC (Know your customer) process using Decision Trees and Naïve Bayesian Classifier in Python to decrease fraud in KYC by at least 25%.‣ Automated the face verification process used for KYC thus reducing the manual work by 30 hrs./week. Designed interactive dashboards using Tableau to effectively portray insights on two year’s aggregated KYC process trends.

‣ Predicted food freshness of food items by running multinomial logistic regression models using 1 million data points from 145 restaurants. Results were used by the restaurant's stock management team to analyze the next stock order.‣ Increased freshness accuracy prediction by 27% by incorporating features from IoT sensors.‣ Improved model robustness by creating linear splines, using feature selection methods, by reducing multicollinearity.

So, I'd like to define myself as a curious person. I like the idea of trying new stuff, whether it is concerned with tech, my subjects, or life in general. I found my passion in Machine learning and Data Science, that's the reason I have joined Drexel, and I am currently pursuing Data science at Drexel University. From the very beginning of my undergrad, I was doing projects related to ML and data science, doing the projects helped me understand the machine learning workflow and things more deeply. Apart from that, I'm a calm and composed person with my head in the game. I try my best to understand what I do and apply them in different areas. I believe I am confident and possess a good smile which I always keep flaunting.

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