Data Scientist with a scientific and environmental analytics background, gained from 13+ years as a Hydrogeologist in water resource management. I translate complex datasets into clear, actionable insights — skilled in SQL, Python, Power BI, Tableau, and Excel, with hands-on experience in dashboards, ETL, feature engineering, and predictive machine learning.
I'm a Data Analyst / Data Scientist with a unique foundation: over 13 years as a Hydrogeologist at South Africa's Department of Water and Sanitation, where I worked with large environmental datasets, statistical analysis, GIS mapping, and stakeholder-facing reporting. I've since transitioned that analytical rigor into the world of data analytics and data science — building predictive machine learning models, designing dashboards, and turning messy real-world data into decisions that matter.
I'm skilled across the full analytics workflow: gathering and documenting business requirements, performing ETL and data cleaning, exploratory data analysis, feature engineering, predictive modelling, and building dashboards that communicate insight to both technical and non-technical audiences. I'm also fluent in leveraging AI tools — ChatGPT, Claude, Lovable, NotebookLM, and Canva — to accelerate analysis, prototyping, and presentation.
A stack built for the full analytics workflow — from raw ingest to executive-ready insight.
End-to-end data analytics and machine learning projects — from raw data to business recommendations.
End-to-end binary classification model predicting 30-day patient hospital readmissions across 3,000 records.
Analyzed 558,811 vehicle sales records to improve future sales, optimize inventory, and guide marketing strategy.
Viewership analytics on 4 months of transactional data across 21 channels and 9,993 viewers to grow the subscription base.
6-month diagnostic sales analysis of 149,116 transactions across 3 store locations for a new CEO.
Analyzed 113,937 rows × 81 columns of borrower, credit, and loan data (2005–2015) to understand borrower risk and loan performance.
Analyzed 8,400 sales records to identify profit drivers across regions and customer segments.
Analyzed 9,995 sales records (2014–2017) to surface geographic, product, and seasonal patterns.
Analyzed 8,904 multi-country orders (Nigeria & South Africa) across 3 outlet types and 13 producers.
Dashboards across three separate datasets (Retail Sales, Shopping Trends, Coffee Shop Sales) to extract insights and support decisions.
University of the Free State
University of the Free State
University of the Witwatersrand
Hands-on training in Python, Machine Learning, SQL, Excel, Power BI, Google Looker Studio, AI tools (ChatGPT, Claude, Lovable, NotebookLM, Canva), and data cleaning for analytics and dashboard creation.
Hands-on training in SQL, Python, Excel, Power BI, Tableau, and data cleaning for analytics and dashboard creation.
Open to Data Analyst / Data Scientist opportunities and collaborations — feel free to reach out.