Tarannum Nisha
Data Scientist @Theory Practice
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WORK HISTORY
Data Scientist @Theory Practice
Vancouver, BC, CA
Identified and obtained minimum viable data, performed data profiling and validation, designed and built ETL pipelines and feature engineering using the Kedro framework.• Implemented end-to-end PySpark pipeline for large-scale feature engineering (∼100M rows), encompassing infrastructure setup, configuration, and scalable code development, resulting in 4× faster runtime.• Optimized email marketing strategy by developing a customer segmentation model combined with customer interest prediction, achieving a 30% higher open rate and 60% boost in click through rate.• Developed demand forecasting models, enabling marketing team to leverage ROI and marginal ROI as key metrics to optimize marketing strategies and drive profitability across multiple channels and markets.• Built atop demand forecasting models, a price and promotion optimizer using Genetic Algorithms to identify the optimal promotion plan within user-defined constraints.• Analyzed price elasticity to optimize pricing strategies and assess demand sensitivity to discount rates.• Collaborated on front-end development to create interactive dashboards used by clients for strategic business planning.• Co-led projects, ensuring timely milestone delivery, and developed client-facing presentations to communicate progress.
EDUCATION
The University of British Columbia
Master of Applied Science , Electrical and Computer Engineering
Malaviya National Institute of Technology Jaipur
Bachelor of Technology, Electronics and Communication Engineering
ABOUT TARANNUM NISHA
Experienced Data Scientist focused on turning messy, real-world data into decisions that move business metrics. I build and ship end-to-end analytics and ML products - spanning data profiling, ETL + feature engineering, modelling, and impact measurement.Highlights include- Built scalable PySpark feature engineering at ~100M-row scale (4× faster runtime)- Developed demand forecasting + GA-based price & promo optimization (up to 96.5% forecast accuracy)- Measured and delivered lift:+8.1% average promo lift across 23 markets;$13M savings in one quarter- Built segmentation + interest prediction for email marketing (+30% open rate,+60% CTR)I hold an MASc from UBC in Network Economics and Optimization (cloud/edge computing), and I enjoy problems at the intersection of modeling, experimentation, and product strategy - ranging from forecasting and optimization to personalization, measurement, and marketplace/growth analytics.
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