Data Scientist

Nwanguma Emmanuel

_

I design and deploy end-to-end machine learning systems — from structured data analysis and feature engineering to validated models and production-ready APIs.

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$ cat engineering_philosophy.txt

01_

Systems Over Scripts

"Accuracy is not production readiness. I build robust, modular architectures that transition seamlessly from notebooks to reliable serving layers."

02_

Reproducibility & Evaluation

"Evaluation-driven ML systems are more important than complex models. I implement strict testing to ensure every model update is backed by rigorous regression metrics."

03_

Deployment-Aware AI Design

"From dataset to Docker container—inference latency and horizontal scalability are first-class citizens in my design process."

$ ls projects/

$ tree ai_system_stack/

Skills organized by system architecture layer.

tree — portfolio
ai_system_stack/
├──Advanced EDA & profiling
├──Feature engineering
├──Statistical testing & experiment design
├──KPI modeling
├──Time series forecasting
├──SQL (PostgreSQL, MySQL, BigQuery)
└──ETL workflows
├──Supervised ML
├──Cross-validation
├──Hyperparameter optimization
├──Imbalanced data handling
├──SHAP interpretability
├──Error analysis
└──PyTorch fundamentals
├──FastAPI model serving
├──REST design
├──JWT authentication
├──Structured JSON outputs
└──Latency-aware inference
├──Docker
├──MLflow
├──Model versioning
├──Reproducible environments
└──CI/CD fundamentals
├──LLM API integration
├──Prompt engineering
├──System prompt design
├──Function/tool calling
├──JSON schema prompting
├──RAG pipelines
├──Embeddings & semantic retrieval
├──Vector databases
├──Prompt regression testing
└──LLM evaluation frameworks

$ git log --contributions

adenhq

BigQuery MCP tool contribution

Position: Collaborating on agent infrastructure and AI tooling.

$ curl medium_feed

Feb 4, 2026

Everyone’s Building AI Wrappers. Few Are Solving Real Data Infrastructure Problems.

#data-analysis#data-science
Feb 4, 2026

Building a Sentiment Analysis System: From 22,632 Reviews to 88% Accuracy

#ai#sentiment-analysis
Feb 3, 2026

A/B Testing Framework: The Data Science Skill That Actually Drives Business Decisions

#business#ab-testing
Feb 2, 2026

Building a Hybrid Recommendation System: Combining Collaborative Filtering, Content-Based, and…

#data-science#python
Oct 22, 2025

Amazon QuickSight Project: Student Enrollment Data Analysis

$ ls thoughts/

Why most ML projects fail in production

The gap between code that runs and systems that perform. Many teams focus on the model (the code) while neglecting the data flywheel, monitoring, and reproducible infrastructure.

Evaluation > Accuracy

A model with 99% accuracy but high latency or bias in long-tail scenarios is often useless. Real-world performance is defined by how well your evaluation metrics align with business outcomes.

LLM reasoning vs prompting

Stop fighting the prompt; start designing the reasoning loop. Better results come from architectural structure (like Chain of Thought or Agentic patterns) rather than just adding more adjectives to a prompt.

Shipping vs experimenting

Exploration is essential, but shipping is the goal. I prioritize MVP architectures that allow for rapid experimentation within a production-ready framework.

$ contact --info

Open to Machine Learning Engineering and Applied Data Science roles.

github.sh linkedin.log send_mail.exe