I build automated data systems that turn raw inputs into decisions, without anyone needing to press a button. Physics background. Engineering mindset. Business-first output.
Selected Work
Real systems. Measurable outcomes. No toy datasets.
A production-grade ML pipeline that fetches live Fantasy Premier League data daily, runs a position-specific ensemble model (XGBoost 50% + Gradient Boosting 30% + Random Forest 20%), and surfaces ranked player recommendations with zero manual input. Custom scoring metrics: FD Index, Delta GI, Delta G_GW. Fully automated via GitHub Actions. Runs every morning without intervention.
Data Engineering
End-to-end automated e-commerce analytics pipeline. Python ingests daily transaction data into BigQuery. dbt transforms raw data through staging and mart layers with automated quality tests. Power BI serves a self-updating dashboard with zero manual steps from source to insight.
End-to-end SQL exploration across 119K+ hotel booking records. Seasonal demand curves, revenue leakage, and high-value guest segments were surfaced and modelled in Power BI. The output directly informed pricing strategy and booking policy adjustments.
Analysed 5,880 customer records to identify the real behavioural drivers of churn. Modelled retention strategies projected to protect $50K in annual revenue. From raw transactional data to a boardroom-ready recommendation.
HR, E-commerce, Finance, and Marketing datasets transformed into interactive, decision-ready Tableau dashboards. Full collection on Tableau Public.
View All Dashboards ↗The Engineer Behind the System
I work as a Data Analyst and Analytics Engineer, focused on designing end-to-end data pipelines, predictive models, and BI systems across operations, logistics, and analytics consulting. My approach is consistent: if a process produces insights, it should be automated, versioned, and repeatable. Not manually maintained.
One example is the FPL Analytics Engine, a fully automated system that runs daily, ingesting data, processing player and fixture metrics, and generating actionable outputs without any manual intervention. It reflects the broader standard I hold every project to: systems over dashboards.
I am currently pursuing an MSc in Financial Engineering at WorldQuant University, strengthening the quantitative foundation behind my work in data systems and predictive analytics. My background in Physics shapes how I approach every problem: structured thinking, modelling complexity, and building from first principles.
Let's Talk
Open to freelance engagements, full-time opportunities, and interesting conversations about data systems. Reach out, the call is free, the insight isn't.
Or email: segunbakare.d@gmail.com · WA+234 903 9453 588