Full Stack Data Science Fellowship

Program Overview The Full Stack Data Science Fellowship is designed to train professionals to build, deploy, and manage complete data science products, not just models. Fellows work across the entire AI lifecycle. By the end of the fellowship, participants will own multiple production-ready AI platforms suitable for enterprise use, startups, or independent SaaS products. Key Responsibilities 1. Business Problem Translation • Convert real-world business problems into data science solutions. • Define KPIs, success metrics, and evaluation frameworks. • Select appropriate ML techniques based on use case constraints. 2. Data Engineering & Feature Development • Design robust data pipelines for batch and real-time ingestion. • Perform feature engineering, handling missing data, outliers, and data drift. • Integrate third-party APIs (CRM, payments, social media, weather, IoT). 3. Machine Learning & Modeling • Build, train, and optimize models using: o XGBoost, LightGBM, Random Forests o Time-series models (ARIMA, Prophet, hierarchical forecasting) o Deep learning (LSTM, GRU) o NLP models (BERT, DistilBERT, LDA) o Anomaly detection (Isolation Forests, Autoencoders) • Evaluate models using appropriate metrics and explainability tools. 4. Backend & API Development • Expose ML models via REST APIs using FastAPI / Flask. • Implement authentication, authorization, and secure data uploads. • Optimize inference latency and scalability. 5. Frontend & Data Visualization • Build interactive dashboards using React / Next.js. • Design executive-ready visualizations: forecasts, risk scores, heatmaps, alerts. • Enable drill-down analytics and scenario simulations. 6. MLOps & Deployment • Version data and models using DVC and MLflow. • Automate training, testing • Deploy solutions on AWS/GCP with Docker and managed inference services. • Monitor model performance, drift, and system health post-deployment.