Data Scientist

picknpay; Cape Town; On-site; ZAR 600,000 - 900,000; Full time

Job summary

Pick n Pay in South Africa is seeking a Data Scientist to join the Analytics and Data Science stream within the Enterprise Data & Analytics division. You will apply advanced analytics and machine learning to retail challenges across South Africa's retail landscape.

You will work with AWS, Snowflake, and Power BI to deliver scalable insights, collaborate with engineering, monetisation, and reporting streams, and translate data into actionable business decisions for customer experience and growth.

Qualifications

• Bachelor's degree (Honours preferred) in a quantitative field.
• 3–5 years of progressive data science and analytics experience.
• Hands-on with Python and SQL for analysis and modeling.
• Experience with cloud platforms, preferably AWS and Snowflake.
• Retail, FMCG, or consumer-facing experience is advantageous.
• Proficiency with Power BI and translating data into insights.

Responsibilities

• Design, develop, and deploy ML models addressing retail challenges.
• Conduct exploratory data analysis to uncover trends and opportunities.
• Build predictive models for merchandising, supply chain, and marketing.
• Develop customer segmentation and lifetime value models.
• Apply statistical techniques to measure and optimise outcomes.
• Translate findings into actionable business recommendations.

Skills

Python

SQL

Power BI

Machine learning

Statistical modelling

Data analysis

Problem solving

Education

Bachelor's degree (Honours preferred) in Data Science/Statistics/Mathematics/Actuarial Science/CS/Engineering/Physics

Tools

Snowflake

AWS

Git

Snowflake Cortex

Snowflake AI

ETL/ELT

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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