Roles and Responsibilites• Querying and Reporting on Fund Raising Data for weekly reports on 5+ events , Blackbaud and platforms• Comparison analysis of yearly performance of Donations, Registrations and Favorite Donors for 5+ events• NLP and Artificial Intelligence Solutions for solving 2 NER Related Problems for maintaining clean database• Coaching for team members with Excel for reporting, charts and use of 3 automated python executables

Worked as Student Research Assistant - 1. Implemented Coaching for Augmented Reality module for Red Bull TV app2. Integrated Front-End to .NET Framework , API's access and Azure for Industry 4.0 - Smart Manufacturing Website

Analytics Consultant - • Develop and Implement Statistical and Machine learning algorithms for Retail Chains. Algorithms: ▪ Customer Prediction ▪ Product Market Fit. & Expenditure deviation▪ Market Basket Algorithms. ▪ RFM, Pareto ▪ Revenue Prediction Month-Wise and Week-Wise. ▪ Customer Lifetime Value (CLV heat map and score) ▪ ARIMA Time series forecasting. • Data Visualization: Power BI completed 4 dashboards Industry standard

Role : BDA-Business Development Associate in Marketing Roles: • Set up appointments with potential customers voice process. • Meet with customers and conduct a presentation on the content. • Convert these customers into loyal customers and set up payment from clients according to their preferences. • Follow up and maintain relationships with good customers. • Setup and conduct marketing campaigns in Lulu and Apartments to help public get more awareness on the subject.

Role : Big Data Analyst, Software Engineer 07.2016 Responsibilities: Manage and conduct Real time Analytics on Retail Data. Develop modules such as Face recognition, Text to Speech and Speech to Text Modules for AI. Basic NLP for Text Matching and Similarity Matching. Text and Social Media Analytics for HTC.BOT Creation, Recommendation systems using Spark Machine learning Algorithms Roles: • Crawled User Review Data for HTC Client against one of their competitor phone products, passed the review data to in house Sentiment Analysis Engine. • Collected Facebook and Twitter Stream Data for the same client. • Retail and Social Media Analytics for a local online Retail chain. Made python builds to execute on a scheduled interval on live Retail chain data to give results. Results include Minimum stock prediction, Recommendation systems Visitor Prediction, Customer by Customer Behavior Analysis. Results were hosted in AWS Server that was available for download for the Retail Members • Basic NLP to make Chabot’s and AI programming. Python UI. In house Face Detection. In house crawling jobs to extract emails from Internet for business team for client prospecting (10000 emails).