Graduated with a Master's degree in Analytics from Northeastern University with a concentration in Leadership. I was a graduate teaching assistant where I have coached 50 students for advance analytical skills Python and Machine Learning. Prior to this, I have worked in Accenture for four years. I am an experienced software engineer who has managed end to end software development lifecycle including requirement gathering, specification design, developing and testing module, and delivering them to production.
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Experience
I have worked in Accenture for four years. During my tenure at Accenture, I started as a software engineer, then moved into business analysis for translating system and business needs into solution requirements and eventually picked up the job function of project coordinator.
Developed Data dictionary and Tables, Function Modules, Application Forms and conducted different testing (Product, Regression, and User acceptance testing) to identify issues in ‘mission critical’ modules for 25 releases.
Performed data extraction, data cleaning, and data formatting for utility billing data and created dashboards to track deployment impact across billing system for >5M users
Interacted with cross-functional teams and stakeholders to present findings.
Managed PM activities across different functional teams(Developers, Testers, and Deployment) for the successful implementation of 3 major SAP projects.
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Projects
1. Developed an artificial personality of a technical geek to quantify the level of interest to generate targeted marketing campaigns. The interest score is generated using a natural language processing technique and simple mathematics. The persona can be extended for other subjects as well such as health, politics.
2. Predicted whether a customer is about to get churn or not for a music service industry using Python. To achieve this, I have performed data checks, data cleaning, data pre-processing, data exploration, feature selection, and built machine learning models to predict customer churn.
3. The purpose of the project was to classify a movie from flop to blockbuster. This is a multi-class classification problem with an imbalanced dataset. After performing data preprocessing, feature selection, and data exploration, I have applied various machine learning algorithms such as logistic regression, KNN, SVM, decision tree, random forest, and gradient descent to predict the movie category.
4. Feedforward neural network model is trained to identify handwritten digits using python.
5. Twitter data is analyzed to perform sentiment analysis on the AWS Sagemaker platform.
6. Designed and developed a data warehouse by integrating data from different sources such as MySQL, Oracle, PostgreSQL, CSVs, and performed transformation with the SSIS tool.
7. Performed statistical data analysis for stocks data for Ford, GM, and Tesla to identify the most valuable company
8. Analyzed health data to determine how BMI is associated with Physical activity