Full Stack Data Science Fellowship

5 days ago


India HEProAI Full time

Role Title: Full Stack Data Science Fellow Duration: 16–24 Weeks (Project & Outcome Driven) Mode: Remote 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: Business problem framing Data ingestion & feature engineering Machine learning model development Backend API creation Frontend dashboards MLOps, monitoring, and cloud deployment 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: XGBoost, LightGBM, Random Forests Time-series models (ARIMA, Prophet, hierarchical forecasting) Deep learning (LSTM, GRU) NLP models (BERT, DistilBERT, LDA) 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, and deployment using CI/CD pipelines. Deploy solutions on AWS/GCP with Docker and managed inference services. Monitor model performance, drift, and system health post-deployment. What Fellows Will Gain: 10+ portfolio-grade full stack data science projects Experience equivalent to Data Scientist / ML Engineer / Applied AI roles Real-world exposure to enterprise-scale AI system design Confidence in deploying and explaining AI products end-to-end Certification and project documentation suitable for hiring managers Career Outcomes: Graduates are prepared for roles such as: Full Stack Data Scientist Applied Machine Learning Engineer AI Product Engineer Data Science Consultant Analytics Engineer AI Startup Founder / SaaS Builder Ideal Candidate Profile: Strong interest in applied data science and AI products Basic Python and SQL knowledge Willingness to work across data, models, APIs, and UI Curious, outcome-driven, and product-oriented mindset



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    Role Title: Full Stack Data Science FellowDuration: 16–24 Weeks (Project & Outcome Driven)Mode: RemoteProgram 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:Business problem framingData ingestion &...


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