Career Timeline | The Art Of Ml

Senior Data Scientist - Target

September 2024 – Present | Bangalore, India

Designed and deployed a pricing optimization system combining ML (XGBoost), regression, and operations research (PuLP), augmented with causal inference and uplift modeling to isolate true incremental impact.→ Result: saves planning teams hours every week and has the potential to generate ~$2M additional profit per pricing cycle.
Part of the Merchandising Promotions team, building ML and deep learning models to estimate promotion lift, redemption, and performance—directly supporting promotion planning decisions at scale.
Applied causal inference frameworks (Difference-in-Differences, RCTs, A/B testing) to validate planner-designed experiments and deliver robust post-promotion impact analysis for senior stakeholders.
Partnered closely with promotion planners and business teams, translating statistical outputs into actionable levers for promotion design.

Led the development of a Deep Learning revenue forecasting model using Heterogeneous Graph Neural Networks, achieving a 25% improvement in prediction accuracy over traditional collaborative filtering approaches in luxury e-commerce.
Built interpretable demand forecasting models across six product categories, reducing MAPE from 17% to ~10%, enabling more confident planning and inventory decisions.
Recognized as an Annual Awardee for technical impact and business value

Worked as a Research Scientist at the Rosen Center for Advanced Computing, applying SARIMA, Random Forests, and clustering to help a pan-US organization navigate post-COVID demand and supply-chain constraints.
Designed and deployed a ticket-management analytics platform for Purdue Computing, featuring clustering, forecasting, and anomaly detection to improve operational efficiency.
Served as an Inferential Statistics tutor and Teaching Assistant for MBA, MS Marketing, and Engineering Management programs—contributing to curriculum design, grading, and lecturing when required.

Built a core Python-based regulatory reporting system used by 1000+ hospitals and clinics across the U.S. to submit Clinical Quality Measures and statistics to the FDA.Applied unsupervised learning (clustering, PCA) to assist clinics and pharmacies in identifying treatment and medication patterns.
Developed a health analytics application predicting disease onset, prognosis, recovery timelines, and patient lifespan with 97%+ reported accuracy, supporting clinical decision-making.
Designed and analyzed A/B experiments to evaluate treatment effectiveness in a diabetes reversal program.

Delivered AI-driven NLP enhancements (auto-categorization, harmful content detection) for the Government of India’s NITI Aayog Women Empowerment platform.
Led development of a resume parsing engine (NLTK, spaCy) for MazoBeam, saving recruiters 100+ hours per week and adopted by multiple global firms.
Built a facial recognition system for one of India’s largest casinos using pre-trained deep neural networks.