Data Science professional with 4 years of experience working with Fortune 100 firms across CPG/FMCG, financial services and travel management. Extensive hands-on experience in developing data-driven solutions for complex business problems.
The accelerated 1-year program spanning over three intensive semesters equipped me with a unique combination of data science skills and analytical acumen while providing opportunity to work with companies like Mall of America, Best buy, Optum etc.
B. Tech in Biotechnology prepared me for research oriented work and provided a platform to develop interpersonal skills.
● Analyzed membership data to design 15+ media campaign for supplier in Malaysia market and measured effectiveness of targeting with A/B testing using metrics like activation and lift, leading to increased redemption rate from 2% to 3%
● Identified suitable technical tools for migration by evaluating speed, scalability and accuracy of Base SAS, Microsoft R and SAS VIYA predicting loan defaulter using GBM (gradient boosting algorithms).
Optimization for Leading Beverage and Brewing company● Led 12-member cross-functional team to develop machine learning based Trade Promotion optimization solution, implementing mixed integer programming and optimized investment allocation of ~$50M by maximizing sales and revenue in prototype engagement of 4 months.
Predictive Analytics for American CPG manufacturer ● Designed, developed and delivered data-driven pricing simulation application based on R shiny using Monte Carlo Simulation and price elasticity model enabling strategic pricing decisions for product category with $368M net sales.
● Built white box price elasticity models for 5 portfolios (80 product group) with accuracy of 85%, reducing MAPE to 15% by leveraging panel regression attributing sales to pricing, distribution and merchandising factors.
● Automated 150+ reports using VBA for 10+ analyst to improve efficiency by reducing 320-person hours to 20-person.
Designed a randomized-controlled experiment using thumbnails of movies to assess the impact of ethnicity on user’s viewing preferences.