About this role
It's fun to work in a company where people truly BELIEVE in what they're doing!
Pick n Pay is seeking a talented Data Scientist to join our Analytics and Data Science stream within the Enterprise Data & Analytics division. This is an exciting opportunity to apply advanced analytics, machine learning and other AI-centric techniques to solve complex business problems across South Africa's retail landscape. Working alongside our Engineering & Architecture, Monetisation, and Reporting streams, you'll contribute to data-driven initiatives that directly impact customer experience, operational efficiency, and business growth. You'll leverage cutting-edge cloud technologies, including AWS, Snowflake, and AI-powered tools to deliver insights and solutions at scale.
Minimum Qualifications Bachelor's degree (Honours preferred) in one of the following fields:
• Data Science • Statistics • Mathematics • Actuarial Science • Computer Science • Engineering (with quantitative focus) • Physics or other quantitative sciences
Experience Required
• 3-5 years of progressive experience in data science, analytics, or related roles • Proven track record of delivering end-to-end data science projects from problem definition through to production deployment • Hands-on experience with Python and SQL for data analysis and modelling • Experience working with cloud data platforms, preferably AWS and Snowflake • Demonstrated ability to work with large, complex datasets • Experience building and deploying machine learning models in business environments • Experience in retail, FMCG, or consumer-facing industries is advantageous
Technical Skills (all are not mandatory, this is a guideline)
• Core: Python (pandas, scikit-learn, numpy), SQL, statistical modelling, machine learning • Cloud & Data Platforms: AWS services (S3, Glue, or similar), Snowflake (required) • AI/ML Tools: Snowflake Cortex, Snowflake AI, or similar cloud-native ML platforms • Visualisation: Power BI (required), experience translating data into business insights • Data Engineering: Basic ETL/ELT concepts, data pipeline development, data quality practices • Version Control: Git or similar
Competencies: Strong problem-solving skills with ability to break down complex business challenges Excellent communication skills - able to explain technical concepts to non-technical audiences Self-motivated with ability to work independently and collaboratively Curious mindset with a willingness to learn new tools and techniques Strong attention to detail and commitment to quality Ability to manage multiple priorities in a fast-paced environment
Key Responsibilities Analytics & Modelling
• Design, develop, and deploy machine learning models and analytical solutions addressing retail business challenges such as forecasting, customer lifetime value, customer churn prediction, pricing optimisation, and promotional effectiveness • Conduct exploratory data analysis to identify trends, patterns, and opportunities across large-scale retail datasets • Build predictive models to support decision-making across merchandising, supply chain, marketing, and operations • Develop customer segmentation and lifetime value models to enhance targeting and personalisation strategies • Apply statistical techniques to measure and optimise business outcomes
Technical Delivery
• Extract, transform, and prepare data from multiple sources using Snowflake, AWS services, and other data platforms • Implement scalable data pipelines and workflows to support analytics and machine learning use cases • Leverage Snowflake Cortex and Snowflake AI capabilities to accelerate model development and deployment • Write and document clean, efficient code in Python, SQL, and other relevant languages • Perform basic data engineering tasks to support analytics workflows, including data quality checks and schema design
Visualisation & Communication
• Create compelling dashboards and visualisations in Power BI to communicate insights to technical and non-technical stakeholders • Translate complex analytical findings into clear, actionable business recommendations • Present findings to senior leadership and cross-functional teams • Document methodologies, models, and processes to ensure reproducibility and knowledge sharing
Collaboration & Innovation
• Partner with data product managers and business stakeholders to understand requirements and frame problems suitable for data science solutions • Collaborate with data engineers, architects, and other analysts to deliver end-to-end solutions • Stay current with emerging techniques in data science, machine learning, and retail analytics • Contribute to the development of best practices and standards within the Analytics and Data Science team
Closing Date: 17 September 2026
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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