Principal Data Scientist

Vodafone

Principal Data Scientist for M-Pesa driving big data analytics and AI product creation. Leading machine learning initiatives for personalized services across a FinTech platform.

Posted 6/9/2026full-timeMidrand • 🇿🇦 South AfricaLeadWebsite

Tech Stack

Tools & technologies

D3.jsHadoopNoSQLPySparkPythonPyTorchScikit-LearnSparkSQLTableau

About the role

Key responsibilities & impact

Create Machine Learning and AI products that provide actionable business insight and drive personalisation for M-Pesa users.
Developing predictive models with large and varied datasets, working with a community of colleagues across Advanced Analytics, technology, and data and customer functions
Development of machine learning models for various areas of the business on the Big Data Platform
Development of prototype code in e.g. PySpark for automated training and scoring of the machine learning models
Machine Learning Model performance tracking and reporting
Uses data visualisation to engage audience in a compelling way, enabling effective storytelling
Work with lead data scientist to deliver key packages of work to meet the needs of business customers
Works in partnership with Big Data Engineering for data ingestion to support use cases
Works in partnership with Big Data Production Data Engineering for model automation and productionising
Contributing to the wider community to enable Machine Learning and AI capability across Vodafone globally.
Manages and takes ownership of a portfolio of work from model development to stakeholder engagement

Requirements

What you’ll need

Bachelor’s or Master’s Degree in quantitative fields like Mathematics, Statistics, Economics, Computer Science Engineering, Artificial Intelligence or related fields (essential)
Professional and/or academic experience in Big Data analytics & deployment of models and algorithms to solve real-world problems (with deep statistical and machine learning modelling expertise)
Familiarity with visualisation tools (e.g. Tableau, Qlik, D3)
Experience working with large datasets (e.g. SQL, Hadoop, Spark, NoSQL)
Proficiency in at least one relevant programming language: Python, R
Experience in major machine learning modelling libraries (e.g., H2O, scikit-learn, PyTorch) and techniques (e.g. random forest, gradient boosting, k-means segmentation, multiple regression, factor analysis, time-series forecasting)

Benefits

Comp & perks

Excellent flexible benefits programme
Commitment to diversity & inclusion

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningpredictive modelingdata visualizationstatistical modelingbig data analyticsprogramming in Pythonprogramming in Rmodel automationdata ingestionmodel performance tracking

Soft Skills

stakeholder engagementcollaborationstorytellingownershipcommunication

Certifications

Bachelor’s DegreeMaster’s Degree