Data Scientist

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! Discover who we are