Job Description

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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