Data Scientist & ML Engineer
I build machine learning and AI solutions that reduce cost, improve targeting, and create measurable outcomes — from raw data to production-ready models.
What I Do
From data pipelines to production ML — end-to-end solutions that solve real problems.
Building and deploying predictive models — classification, regression, clustering — that help you act on what the data is telling you.
Building language-powered products — sentiment analysis, text classification, LLM-driven tools for marketing, HR, and customer experience.
Designing and building reliable data pipelines, ETL workflows, and cloud infrastructure so your teams always have clean, accessible data.
Translating business questions into clear, interactive dashboards and analysis that your entire team can understand and act on.
Taking models from notebook to production — containerised APIs, AWS-hosted applications, and monitoring so your ML actually runs in the real world.
Rigorous statistical analysis for clinical trials, forecasting, and A/B testing — the kind of analysis that holds up to scrutiny.
Selected Work
Real problems. Real data. Real outcomes.
Built a real-time streaming pipeline that extracts video and channel metrics from the YouTube Data API, processes events through Apache Kafka and Spark Streaming, and loads them into BigQuery — with Apache Airflow orchestrating scheduling, monitoring, and error alerting.
Content teams can now act on engagement trends as they form — not days after the opportunity has passed.
View Project
Built an LLM application that scores marketing content against user personas, flagging misalignment and generating tailored recommendations before campaigns go live.
Reduces wasted ad spend by surfacing mismatched messaging before campaigns launch — improving relevance and conversion potential.
View Project
Trained and deployed a multi-class text classifier that identifies whether a person believes, denies, or is neutral about climate change — directly from social media data.
Enables brands to segment audiences by stance and tailor messaging accordingly — cutting through noise to reach the right people.
View Project
Designed a personalised recommendation engine using collaborative filtering, then deployed it as a live application on AWS — end-to-end.
Demonstrates full-stack ML capability: from model design to production cloud deployment serving real users.
View Project
Modelled Spain's fossil-to-renewable energy shortfall to help the government quantify the gap and build a credible transition roadmap.
Provided actionable forecast outputs to support national energy policy planning — showing how ML can inform high-stakes decisions.
View Project
Analysed workforce data to identify the root causes of chronic absenteeism and produced a set of evidence-backed HR policy recommendations.
Surfaced patterns invisible to management — giving HR teams a clear, data-led starting point to reduce employee absences.
View Project
Conducted rigorous dose-response modelling comparing two oral insulin treatments (Auralin and Novodra) to determine clinical equivalence and optimal dosing.
Delivered statistically sound analysis to support drug efficacy evaluation — the kind of rigour that matters in regulated healthcare environments.
View Project
Mined group chat history with NLP techniques to uncover engagement patterns and produced a communication strategy with concrete improvement recommendations.
Proved that even unstructured messaging data holds actionable signal — a replicable method for community and product teams.
View Project
About Me
I'm Ridwan — a data scientist with hands-on experience across the full ML lifecycle, from writing the first SQL query to deploying models on AWS.
My interest in data started with a simple observation: most decisions in business are still made on instinct when the data to make them better already exists. That gap is where I work. Over the past four years I've built classifiers, recommendation systems, forecasting models, and LLM-powered tools — always with the same goal: make the output useful to someone making a real decision.
I graduated in the top 5% of the ALX Data Analytics program as a Gold Fellow of The Room. I'm proficient in Python, SQL, Power BI, and AWS, and I'm comfortable working across the stack — data wrangling, model building, cloud deployment, and communicating findings to non-technical stakeholders.
Download Resume (PDF)Get in Touch
Whether you're looking to hire a data scientist, need a freelance ML engineer, or want to collaborate on something — I'd love to hear from you. I typically respond within 24 hours.