Ramya Subramanya

Developed and deployed a - 1) recommendation engine to identify key factors affecting the delivery process, 2) lead time forecasting model improving prediction accuracy, 3) a statistical model for safety stock forecasting, increasing accuracy 4) Mentored junior data scientists and analysts, fostering skill development in data science techniques, MLOps best practices.

Leveraged analytical and programming skills to collect, process, and interpret large-scale datasets, optimizing decision-making in workforce analytics. 1) Built predictive models using ML to forecast supply chain demand and predict employee attrition, improving retention strategies 2) Developed time series models, capturing complex data patterns for demand forecasting, workforce planning, and absenteeism prediction 3) Performed hyperparameter tuning to enhance diversity hiring analytics