I'm Ishmeen,
a Data Scientist building intelligent systems at the intersection of ML, NLP & Deep Learning.

• Developing healthcare digital twin prototype integrating multi-source physiological datasets for patient-level treatment simulation.
• Constructing structured and time-series feature pipelines to support predictive modeling experiments.
• Delivered Advanced Excel workshop to 25+ MS Data Science students; currently developing an Advanced SQL workshop.

• Analyzed 5,000+ vehicular trajectories using spatiotemporal modeling, validating data quality and consistency with <2% error.
• Engineered behavioral and efficiency metrics (gap acceptance, yielding, speed compliance) across 10+ maneuver types, improving model classification accuracy by 18%.
• Designed and evaluated ML-based decision systems using fine-tuned BERT and retrieval-augmented pipelines, achieving a 22% accuracy lift over rule-based baselines.

• Automated FX deal valuation pipeline using QuantLib with REST API integrations, reducing manual valuation processing time by 75%.
• Built Django-based visualization dashboard with CI/CD deployment adopted by 40+ consultants across practice, enhancing reporting efficiency.
• Analyzed RBI vs. NBFC climate risk policies, identifying 3 major gaps with actionable mitigation proposals.
• Presented automation framework and policy recommendations to 3 Partners and 15+ senior leaders.

Published research at the intersection of NLP, Quantum Computing, and Blockchain in IEEE Xplore and Springer.

Relevant Coursework: Machine Learning, Statistical Inference, Causal Inference, Forecasting, Applied Deep Learning, Data Visualization.

Collaborating with Columbia Business School MBA students through Analytics in Action to solve real industry challenges.

CGPA: 9.66/10.0 — Graduated in the top 10% of the class. Core coursework in Data Structures, Operating Systems, Machine Learning, Big Data Analytics, AI, Blockchain, and Cryptography. Published 5 research papers during undergraduate studies.