Shashank Tyagi

Engineered a scalable, GCP-hosted data ingestion pipeline using Python to extract and process creator insights from Instagram Graph API and TikTok API, enhancing data accessibility and supporting real-time analytics across marketing platforms. Developed and deployed a predictive ML model via FastAPI to forecast creator rate cards using performance and engagement metrics, automating pricing decisions and reducing manual analysis time by 70%. Delivered campaign ROI forecasting system using regression models to predict engagement outcomes, optimizing marketing spend efficiency by 25%.

Engineered a scalable, GCP-hosted data ingestion pipeline using Python to extract and process creator insights from Instagram Graph API and TikTok API, enhancing data accessibility and supporting real-time analytics across marketing platforms. Developed and deployed a predictive ML model via FastAPI to forecast creator rate cards using performance and engagement metrics, automating pricing decisions and reducing manual analysis time by 70%. Delivered campaign ROI forecasting system using regression models to predict engagement outcomes, optimizing marketing spend efficiency by 25%.

Led the enhancement of lead conversion processes by designing and deploying supervised machine learning models (Random Forest, Logistic Regression) for lead scoring, resulting in a 15% increase in conversion rates. Automated end-to-end data pipelines to deliver timely and accurate portfolio management reports, significantly reducing manual effort and turnaround time.