Sarthak Shandilya

I’m a Data Scientist and ML Engineer with end-to-end project experience spanning predictive modelling (AUC ~0.97), time series forecasting (~60% error reduction), and deep generative modelling (Conditional DCGAN, WGAN-GP).

I build models that don’t just perform in notebooks—I focus on stability, reproducibility, and deployment. For example, MorphGen trained a Conditional DCGAN on CelebA (~200K images), used WGAN-GP with gradient penalty and spectral normalisation, and achieved stable convergence without mode collapse.

On the forecasting side, I delivered DemandIQ with ARIMA, SARIMA, Prophet, and XGBoost over 1M+ retail records, reducing forecast error ~60% vs. a naive baseline. I also containerised pipelines with Docker, tracked experiments with MLflow, and exposed insights via Streamlit for non-technical stakeholders.

For GTM analytics, PortIQ combined EDA and SQL on 1,200 AIS vessel records with 27 domain-specific GTM features, reaching AUC ~0.97 (5-fold cross-validation). I’m also open-sourcing my workflows (e.g., MorphGen) and using disciplined experimentation practices through MLflow to support scalable, data-driven decision-making.