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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