My most recent work experience includes working at EY as a Senior Data Scientist with my project
focused on credit and risk analytics. My dissertation research finds direct application in online retail and
subscription entertainment platforms like Amazon and Netflix. My dissertation work is at the
intersection of four fields: (1) online recommender systems (2) consumer choice models, (3) supply
chain models, and (4) assortment planning. In my current research I develop mathematical models for
personalized online recommendations capturing different context-effects associated with individual user
selection behavior. In order to analyze the developed mathematical decision tools I am using big data
sets (MovieLens, Amazon Product Review, Online Retail dataset at UCI Repository etc.). My thesis
includes an in-depth, iterative and methodical exploration of some of the very Big-Datasets available for
consumer preference ratings. I achieve significant insights for online recommender systems through a
comprehensive statistical analysis from the results generated by my developed mathematical models
both via simulated data and real-world datasets mentioned. My future research incudes incorporating
better mathematical representations of consumer behavior as a result of choice-context via data-driven/
non-parametric approaches and using data analytics to improve consumer choice models. My
background in engineering, work experience in ERP consulting, a Masters in Supply Chain, and my
current research motivates the inter-disciplinary nature of my research interests. My 2 years of teaching
assistant experience in statistics, operations research, optimization and simulation equipped me with indepth
conceptual clarity required to apply these skills to real world problems.