Aashna Srivastava

Implemented machine learning predictive model in Azure Databricks to automate demand planning process and supply chain analytics for various European markets. Built forecasting models using an ensemble of algorithms like XGBoost, Random Forest, ARIMA. Improved demand forecasting accuracy by 8-10% compared to traditional methods. Streamlined data ingestion for a leading Pharma client using a multi-step pipeline. Worked on Marketing Mix Modelling to optimize channel spends using LPP. Responsible for Pricing Analytics Architecture including Customer Segmentation using K-Means clustering and Dynamic Pricing with Light GBM.

Performed exploratory data analysis using Python libraries like Pandas, Sklearn, SciPy for data science on bank stock prices and their predictive modeling. Predictive Analysis of the insurance industry data using the Random Forest model. Analyzed user behavior for advertisement data using Logistic Regression Models. Customer segmentation using Machine Learning Models.

Compilation and manual testing of Server Management tools such as openssh, Perl, Python, sudo on various Linux and Unix servers. Created scripts in shell for automating the testing of SM tools. Development of PowerShell Script using WMI classes.