PRICHARD MUMBA
Summary
Results-oriented data analytics professional with expertise in managing complex analytics projects and delivering insights through advanced SQL, Python, and Excel. Utilises AI-assisted tools for predictive modelling and data storytelling across platforms like Power BI, Tableau, and Looker Studio. Collaborates with cross-functional teams to align objectives and uphold data standards, driving commercial improvements through evidence-based decision-making.
Work History
Data Analyst
5 Months- Analysed environmental and operational datasets using SQL and Python to support waste management and sustainability initiatives.
- Leveraged AI-assisted analysis tools to discover patterns and identify optimisation opportunities in waste stream data.
- Developed and maintained Power BI dashboards monitoring KPIs such as collection adherence, contamination rates, and material repurposing performance.
- Produced benchmarking and trial analysis reports that improved recycling accuracy by 15%.
- Partnered with product, engineering, and account management teams to analyse client trial outcomes and identify key drivers of platform adoption.
Business Intelligence Analyst
3 Years 2 Months- Executed complex SQL queries and automated ETL processes to integrate data from sales, procurement, and inventory systems, enhancing data accessibility for analysis.
- Analysed historical supply chain data using statistical methods and AI-powered tools to identify operational bottlenecks and improve inventory planning.
- Produced performance reports for senior leadership, highlighting KPIs such as turnover, cost per metric ton, and inventory turnover ratio, enabling informed decision-making.
- Conducted margin and cost analysis, supporting pricing and profitability optimization.
Teacher of Mathematics & Statistics
9 Years 5 Months- Delivered curriculum-aligned mathematics and statistics instruction with a focus on numerical reasoning and data literacy.
- Evaluated teaching strategies using data-driven approaches to identify effective practices and enhance student learning outcomes.
- Analysed and reported assessment data to track student performance and inform targeted interventions, enhancing support for individual learning needs.
Education
Master of Science - Data Science & Artificial Intelligence
Online
MSc Data Science and Artificial Intelligence, University of Liverpool (in progress)
A postgraduate degree covering machine learning, data analysis, cloud computing and research methods, with a strong focus on applying techniques to real-world data.
Coursework so far includes analysing FEMA’s 2023 National Household Survey wildfire data in Python and pandas, assessing data quality with pandas and scikit-learn, and writing a comparative essay on cloud security compliance in AWS and Azure. In the Machine Learning in Practice module, I’m working on a group project that predicts London air pollution (NOx, PM2.5 and PM10) from the London Atmospheric Emissions Inventory 2019. The work covers profiling, cleaning and aggregating 14 linked datasets, then selecting, building and evaluating predictive models.
My dissertation analyses GCSE attainment in UK secondary schools, which brings together my data science training and my background in education
Postgraduate Program - Data Science & Business Analytics
Postgraduate programme in Data Science, Great Learning (2024)
An applied data science programme covering Python, business statistics, supervised and unsupervised machine learning, ensemble methods and model tuning. Each module ended with a hands-on project built around a real business problem, seven in total.
Projects included A/B testing for a news portal’s landing page, regression-based pricing for refurbished devices, cancellation prediction for a hotel chain, visa approval modelling with ensemble classifiers, failure prediction for wind turbine generators using sensor data, and clustering of stocks by financial attributes.
Key skills developed: exploratory data analysis, hypothesis testing, linear and logistic regression, decision trees, random forest, boosting (AdaBoost, gradient boosting, XGBoost), handling imbalanced data, hyperparameter tuning with GridSearchCV, K-means and hierarchical clustering, and turning model results into business recommendations.
ACCA Diploma - Accounting and Business (RQF Level 4)
Bachelor of Science with Education - Mathematics & Statistics
Bachelor of Science with Education (Mathematics), University of Zambia (2009 to 2013)
A four-year degree combining advanced mathematics with teacher training. The mathematics core covered statistics and probability, mathematical statistics, linear models and design of experiments, linear algebra, real analysis, complex analysis, group and ring theory, topology and six levels of mathematical methods. The first year also included introductory physics and chemistry.
The education strand covered mathematics teaching methods, educational psychology, sociology of education, education policy and educational administration and management, ending with a supervised school teaching placement.
The degree gave me a strong grounding in statistical inference, regression and experimental design, which underpins my later work in data analysis and machine learning, and the training that launched my career as a mathematics teacher.
Skills
Certification
- Databricks Academy – Data Engineering with Databricks
- Power BI for Business Intelligence | Tableau for Data Analytics & Business Intelligence
- IBM Professional Certificate in Data Science
- Google Cloud Platform – BigQuery & Data Analytics | Looker and Looker Studio
- Google Sheets Advanced | AI-Assisted Data Analysis Tools & Techniques
PROJECT PORTFOLIO
Postgraduate programme in Data Science, Great Learning (2024)
An applied data science programme covering Python, business statistics, supervised and unsupervised machine learning, ensemble methods and model tuning. Each module ended with a hands-on project built around a real business problem, seven in total.
Projects included A/B testing for a news portal’s landing page, regression-based pricing for refurbished devices, cancellation prediction for a hotel chain, visa approval modelling with ensemble classifiers, failure prediction for wind turbine generators using sensor data, and clustering of stocks by financial attributes.
Key skills developed: exploratory data analysis, hypothesis testing, linear and logistic regression, decision trees, random forest, boosting (AdaBoost, gradient boosting, XGBoost), handling imbalanced data, hyperparameter tuning with GridSearchCV, K-means and hierarchical clustering, and turning model results into business recommendations.