Dec 2020 – Mar 2021 · 3 mos

• Deployed unsupervised learning techniques like K-means clustering for customer segmentation based on product usage patterns, purchasing behavior, and market dynamics. Leveraged this analysis to identify loyal customer segments versus those at risk of switching to a therapeutic area team at a major US-based pharmaceutical company.
• Developed predictive models using supervised machine learning algorithms such as decision trees and random forests to determine key drivers influencing sales performance for a product line at a US-based healthcare firm. Quantified variable importance of input features to extract actionable insights on factors impacting desired business outcomes.

Jul 2018 – Dec 2020 · 2 yrs 5 mos

• Formulated data-driven strategies to optimize field force promotions for over 500 sales representatives by combining customer segmentation analysis and promotional response modeling, maximizing revenue generated per physician visit.
• Constructed comprehensive data-driven marketing plans spanning multiple channels by developing probabilistic models to guide strategic promotional mix and standardize execution.
• Built predictive models using Python to forecast the adoption of a new pharmaceutical drug among target customers. Deployed ensemble techniques and logistic regression, attaining 89% prediction accuracy for the adoption probability model.

Jan 2017 – Jun 2017 · 5 mos · New Delhi Area, India

• Worked on a 'Smart Insole' for Diabetic Patients for Pressure mapping and continuous feedback using Pressure Sensors and Materials Research for the existing Insole.

Jun 2016 – Jul 2016 · 1 mo

• Project: Rectification and analysis of water intrusion in vehicle using shower test.
• Suggested various ways to avoid leakage due to different manufacturing defects like welding defects, paint defects etc.
• Also worked on an Upcoming Model IGNIS.