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Seun193/README.md
Seun Obisesan — QA Automation, Energy Economics and Data Analysis Profile views

> whoami

QA Engineer
├── Test automation & release validation
├── API, data & integration testing
├── Decision-system validation
├── Python & SQL engineering
└── Quantitative energy-market research

I work at the intersection of software quality, automated validation, decision systems, and quantitative analysis.

My engineering work focuses on turning requirements, data flows, APIs, business rules, and model outputs into testable systems with reproducible evidence. Alongside QA engineering, I maintain independent research and engineering labs covering banking decisioning and Finnish/Nordic electricity markets.


> ls ./featured-work

🏦 Next Best Action Decisioning Lab

A banking-style decisioning and QA engineering environment built around customer data, eligibility rules, action scoring, ranking, explainability, API behaviour, and release-readiness validation.

Current engineering coverage

  • Python + FastAPI + SQLite
  • business-rule validation
  • API contract & negative testing
  • database-to-API reconciliation
  • decision arbitration
  • model-to-decision validation
  • CI regression & release gates
  • full-population reconciliation
View Next Best Action Decisioning Lab

⚡ Finland Power Market Lab

Independent quantitative research into Finnish and Nordic electricity markets, with emphasis on reproducible analysis and evidence-led modelling.

Research areas

  • electricity-demand behaviour
  • spot-price formation
  • demand flexibility
  • forecast error & imbalance
  • balancing-market behaviour
  • econometric modelling
  • robustness testing
  • reproducible analytical pipelines
View Finland Power Market Lab

> cat /etc/engineering-stack.conf

Languages, Engineering & Data

Python, JavaScript, Node.js, SQLite, FastAPI, Docker and Git

Testing, CI & Development Tools

Postman, Jenkins, GitHub Actions, Bash, PowerShell, PyCharm and Google Cloud

Test Automation

Robot Framework Playwright pytest Newman SOAP UI

Data & Research

pandas NumPy scikit-learn Econometrics Energy Economics


> ./run_diagnostics.sh

$ ./run_diagnostics.sh --scope engineering

[CHECK] Requirements   → testable acceptance criteria
[CHECK] APIs           → contracts, errors & integration behaviour
[CHECK] Data           → integrity & source-to-output reconciliation
[CHECK] Decisions      → eligibility, ranking & explainability
[CHECK] Models         → output validation & robustness
[CHECK] Releases       → regression evidence & CI quality gates

Principle: verify behaviour, trace evidence, expose failures.

Engineering checklist — illustrative, not a live test report.



> cat ./quality-philosophy.md

I like testing systems where correctness depends on more than a successful HTTP response:

requirements
    ↓
data integrity
    ↓
business rules
    ↓
API & integration behaviour
    ↓
decision / model output
    ↓
regression evidence
    ↓
release confidence

My preferred approach is to validate important behaviour at multiple independent boundaries, reconcile outputs back to source data, include negative scenarios, and make failures visible in CI.


> ls ./research-interests

software-quality/
├── test automation
├── API & integration validation
├── data quality
├── decision-system testing
└── release readiness

energy-economics/
├── Finnish & Nordic electricity markets
├── demand behaviour
├── spot-price formation
├── flexibility
├── imbalance
└── balancing markets

> git log --graph --contributions

GitHub contribution grid snake animation


Building systems that are measurable, testable, explainable, and reproducible.

Pinned Loading

  1. NextBestAction-Decisioning-Lab NextBestAction-Decisioning-Lab Public

    Banking Next Best Action decisioning lab with FastAPI, SQLite, pytest, GitHub Actions, arbitration, propensity validation, end-to-end data-flow testing, and automated release gates.

    Python

  2. FinlandPowerMarketLab-Showcase FinlandPowerMarketLab-Showcase Public

    Applied Energy Economics research on Finnish electricity demand, spot prices, flexibility, forecast error, imbalance and balancing market

    Python