Kimia Sajjad
Msba Capstone Analytics Consultant @Trimble Inc.
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WORK HISTORY
Msba Capstone Analytics Consultant @Trimble Inc.
Developed a multi-stage entity resolution pipeline integrating weighted scoring, probabilistic matching, and LLM re-ranking to resolve product identities across large-scale enterprise datasets (NDA)- Built a custom weighted scoring model evaluated using MAP@K metrics to benchmark and drive iterative improvements- Designed and managed a SQLite relational database (100K+ records) with custom ingestion workflows- Integrated LLM-assisted matching with chain-of-thought prompting to improve precision on ambiguous record pairs- Delivered technical milestones to an enterprise client with a 5-person cross-functional team across a full project lifecycleMSBA Capstone Project – Haslam College of Business
EDUCATION
Haslam College of Business at the University of Tennessee
Bachelor of Science in Business Administration, Marketing
Haslam College of Business at the University of Tennessee
Master's degree, Business Analytics
ABOUT KIMIA SAJJAD
I\'m an M.S. in Business Analytics candidate at the Haslam College of Business, University of Tennessee with a background in marketing, international business, and nearly a decade of client-facing work across the U.S. and Europe. Over the past year I\'ve built end-to-end machine learning pipelines, trained neural networks on 19M+ row datasets, developed NLP and generative AI products that surfaced insights traditional reporting missed, and delivered analytics solutions to an enterprise client, all while finishing my degree. My foundation in marketing and strategy means I\'m equally comfortable writing production-level code and presenting findings to senior stakeholders. I care about the business question as much as the technical answer, which tends to make the work more useful. I\'m trilingual in English, Farsi, and Spanish, and I\'m especially passionate about applying analytics to real business problems: building predictive models, designing scalable ML pipelines, uncovering insights hidden in messy data, and helping organizations make faster, smarter decisions. Recent focus areas include large language models, RAG architectures, agentic frameworks, reinforcement learning, and time-series forecasting, applied to real enterprise problems at scale. My top skills include Python, R, SQL, machine learning, statistical modeling, data visualization, dashboarding, and translating complex analysis into clear business recommendations. Open to connecting.
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