Full Stack Data Science Fellowship

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