I make data trustworthy, then I make it predictive.
Engineering data. Modelling outcomes.
Data Scientist and Analytics Engineer. Based in Manchester, UK. Open to Germany, EMEA, the UK, and remote roles. MSc Data Science at Salford, finishing May 2026.
Python
R
SQL
PySpark
dbt
Dagster
scikit-learn
HuggingFace
PyTorch
Power BI
Sources → Ingestion → Transformation → Serving → Consumption
Three disciplines, one tool belt.
Applied machine learning.
I build prediction models and recommender systems on top of clean data layers. XGBoost for the modelling, SHAP for explainability, MLflow for tracking, PySpark for scale. Football scouting (MSc thesis), Steam game recommendations, churn and growth prototypes on customer intelligence data. Never black boxes.
scikit-learnSHAPPySpark
MLflowData engineering.
I build the orchestration and ingestion layer. Dagster and Airflow for orchestration, Python for extractors and transformations, PostgreSQL/BigQuery/Snowflake for the warehouse. Schedules, contracts, observability. The unglamorous spine that everything else depends on.
AirflowPythonPostgreSQLAnalytics engineering.
I model business logic into curated tables analysts and downstream apps actually use. dbt for transformations, Kimball for the modelling pattern, version-controlled and tested. Raw to staging to intermediate to marts.
SQLKimball
GitEleven projects that explain what I do better than my CV can.
Account Intelligence Platform
A greenfield Account Intelligence platform built over 16 weeks at Force24 for CSMs, accounts, and stakeholders, shipped with the engineering team into a live production environment. I owned the data layer end to end, integrated it into the FastAPI service through endpoint changes and Redis caching, and shipped features and enhancements across the Angular frontend. Built on the principle that dashboards should drive action, not just display data. Confidential, sanitised case study.
PostgreSQLPython
Redis
Angular
Agentic ELT Data Platform for Customer Intelligence
MSc dissertation in a live B2B SaaS environment under NDA. End-to-end JSONB-first ELT platform and three-model churn intelligence stack (survival, XGBoost via PostgresML, and DR-Learner causal inference), surfaced via FastAPI, Angular, and an MCP endpoint for agentic LLM access. Over 1 million records ingested across multiple vendor APIs, 48 dbt models.
PythonPostgreSQLdbtDagsterPostgresMLeconmlMCP
Pharmaceutical Side Effect Classification
Production-grade Python package classifying free-text adverse-event descriptions into a MedDRA-style taxonomy of ten clinical categories across 11,825 marketed medicines. sklearn Pipeline with ColumnTransformer, joblib-serialized end to end, pytest fixtures, GitHub Actions CI matrix on Python 3.10, 3.11, 3.12.
Pythonscikit-learn
pytest
GitHub ActionsNotes from the work, in the open.
Articles and posts as they publish. The most recent ones live on LinkedIn.
Summary.
Seven years in data, across retail banking in Edo, Nigeria, mortgage analytics in Abuja, Nigeria, healthcare marketing in Istanbul, Türkiye, and now an MSc dissertation in Manchester, UK. The through-line has always been the same. Build the data layer first. Ship the dashboard second.
Two Masters (Salford, ongoing; Bahcesehir University in Türkiye, 3.67 GPA) and an engineering undergrad at the University of Benin in Nigeria. Nine certifications across Google Cloud, IBM, and LinkedIn Learning. Two years and nine months as a Microsoft Learn Student Ambassador.
What I care about in 2026: dbt, dimensional modelling, CI/CD for data, making analysts faster, and building ML systems that explain themselves.
Let's talk.
If you are hiring for an Analytics Engineer or Data Scientist role in Germany, EMEA, the UK, or remote, I would like to hear about it. If you are a fellow practitioner and want to swap notes on the modern data stack, I would like that too.