Job Description
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 Saa S 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, Io T). 3. Machine Learning & Modeling:
Build, train, and optimize models using:
XGBoost, Light GBM, Random Forests
Time-series models (ARIMA, Prophet, hierarchical forecasting)
Deep learning (LSTM, GRU)
NLP models (BERT, Distil BERT, 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 Fast API / 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 / Saa S 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
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 Saa S 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, Io T). 3. Machine Learning & Modeling:
Build, train, and optimize models using:
XGBoost, Light GBM, Random Forests
Time-series models (ARIMA, Prophet, hierarchical forecasting)
Deep learning (LSTM, GRU)
NLP models (BERT, Distil BERT, 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 Fast API / 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 / Saa S 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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