Gayatri R

I am a results-driven data scientist with deep experience building production ML and data engineering systems across fintech, edtech, and aerospace domains. I design end-to-end solutions—from automated ETL and feature engineering to model training, deployment, and monitoring—that deliver measurable business impact.

My work has improved customer retention, risk classification, and operational reliability: examples include a 25% lift in customer retention from churn models, 30% better risk segmentation in credit scoring, and 98%+ accuracy in fraud and fault detection systems. I blend classical statistics with modern deep learning and LLMs to solve classification, forecasting, recommendation, and anomaly-detection problems.

Technically, I deploy models and APIs at scale using Docker, FastAPI, Kubeflow, MLflow, SageMaker, Vertex AI, Kafka, and cloud platforms; I build pipelines with Spark, Airflow, dbt, and Snowflake; and I visualize and operationalize results in Tableau, Power BI, Streamlit, and Dash. I prioritize reproducibility, CI/CD, and observability in ML workflows.

I mentor junior data scientists, collaborate cross-functionally with product and compliance teams, and focus on delivering actionable insights that drive executive decision-making and operational improvements.