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Jayanthi Jayakumaran
Senior Data Scientist @Intuit
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WORK HISTORY
Senior Data Scientist @Intuit
US
Guided end to end machine learning initiatives using Python, TensorFlow, and PyTorch, raising model throughput by 35% across high volume workflows.Directed LLM powered automation pipelines using LangChain and RAG frameworks, trimming manual analysis efforts.Presented probabilistic forecasting for revenue prediction, raising quarterly planning accuracy with $11M cumulative impact.Refined customer behavior scoring via ensemble methods like XGBoost and Random Forest, boosting segmentation precision.Reinforced MLOps standards using MLflow, Kubernetes, and CI/CD pipelines, reducing deployment friction by 43%.Orchestrated NLP centric systems leveraging GPT for document processing, compressing review time from hours to minutes.Bolstered experimentation workflows using A/B testing, causal inference, and Bayesian evaluation, leading to 20% improvement in feature level decision quality.Integrated BigQuery, Snowflake, Databricks pipelines with ML models, enabling subsecond analytics for intelligence dashboards.Constructed real time risk monitoring algorithms for fraud initiatives, preventing projected losses exceeding $4.7M annually.Oversaw cross functional data reviews with engineering and product teams, resulting in 23% reduction in data irregularities across production systems.
EDUCATION
Anna University
Bachelor's degree
Florida International University
Master's degree, Data Science / Biostatistics Data Analytics
ABOUT JAYANTHI JAYAKUMARAN
Data Scientist with 5+ years of experience building scalable machine learning, NLP, and AI systems that drive measurable business impact. Strong in end-to-end ML pipelines, from data engineering and experimentation to real-time deployment. Expertise in LLMs, RAG pipelines, predictive modeling, A/B testing, and MLOps across cloud platforms (AWS, GCP, Azure). Known for translating complex analytics into clear insights, improving revenue forecasting, risk detection, and decision quality.🤝 Collaborative, impact-driven, and passionate about applied AI at scale.
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