Data Scientist

SUMMARY:
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POSITION INFO:
Mid-Level Data Scientist \/ Data Science Architect \/ Data Science Developer – | Bryanston, Centurion or Gqeberha | Permanent Turn complex data into intelligent solutions that solve real business problems. This is an opportunity to combine data science, machine learning, data engineering and product development to create scalable analytical solutions with measurable commercial value. The successful candidate will contribute to the design, development and optimisation of data science solutions and analytical products, with a particular focus on the retail sector. Working across data architecture, pipeline engineering, predictive modelling and machine learning, you will help transform diverse data sources into reliable, actionable insights and production-ready solutions. This is a hands-on technical role suited to a data professional who enjoys moving beyond analysis to build, deploy and improve data-driven products. You will work with multidisciplinary teams and stakeholders across internal business units and external clients, translating business challenges into practical technical solutions. The role also offers opportunities to investigate new data sources, experiment with emerging technologies and contribute to the technical direction of innovative analytics products. Our client is an established South African intelligent solutions company specialising in data management, geographic information systems, analytics and software development. With a collaborative, flexible and innovation-focused culture, the organisation develops integrated data and technology solutions for a range of business applications. The successful candidate will join an environment that values participation, adaptability, teamwork and the delivery of meaningful client outcomes. What You'll Do Design and improve data architectures that support advanced analytics, machine learning and scalable product delivery. Build, optimise and maintain robust data pipelines for ingesting, transforming and delivering data from multiple internal and external sources. Develop, deploy, monitor and maintain machine learning models and analytical solutions in production environments. Apply statistical methods and machine learning techniques to solve practical business problems. Develop innovative data products, analytical features and commercially valuable proofs of concept. Investigate new data sources, technologies and analytical approaches to identify opportunities for product development and business growth. Translate complex business requirements into scalable technical solutions in collaboration with internal stakeholders and external clients. Maintain and improve existing predictive models, analytical products and decision-support tools. Design and optimise data models, ETL\/ELT processes and data workflows for performance, reliability and scalability. Use SQL, Python and Power BI to develop, analyse, interpret and communicate data-driven insights. Apply software engineering best practices, including version control, testing, code quality and technical documentation. Contribute to architecture decisions, solution design and continuous improvement initiatives. Collaborate with product, engineering and business teams in an Agile development environment. Share knowledge through peer coaching, technical discussions, presentations and broader business updates. Manage priorities, estimate work and deliver high-quality outputs within agreed timelines. What You Bring A minimum of three years' relevant experience in data science, analytics engineering, machine learning engineering or a related discipline. A BSc Honours or MSc in Statistics, Mathematics, Computer Science, Data Science, Engineering or a related field is preferred. Very high proficiency in SQL, Python and Power BI. Demonstrated experience designing data solutions and building robust, scalable data pipelines. Practical experience developing, deploying and maintaining machine learning models or advanced analytics solutions in production. Strong understanding of statistical concepts, predictive modelling and machine learning techniques, with demonstrated business application. Experience with data architecture, data modelling, ETL\/ELT processes and performance optimisation. Ability to integrate multiple data sources and design scalable analytical workflows. Experience taking analytical prototypes through to production-ready solutions. Familiarity with software engineering principles, version control and testing practices. Strong data interpretation, problem-solving and analytical skills. Professional technical writing, reporting, documentation and communication skills. Proficiency in Microsoft Office, with advanced Excel advantageous. Familiarity with Agile principles and their application in data science or product development. A client-centric approach and the ability to communicate effectively with technical and non-technical stakeholders. Strong organisational skills, attention to detail and the ability to prioritise work and meet deadlines. A hands-on, solutions-driven mindset, supported by a commitment to continuous learning and innovation. Experience working with geographical, GIS-related or logistical data is advantageous. Exposure to retail or insurance analytics is advantageous. What Success Looks Like Scalable, reliable data pipelines and architectures support the delivery of analytical products. Machine learning models and analytical solutions perform effectively in production and are maintained as business needs evolve. Complex data is transformed into actionable insights that support client decisions and commercial outcomes. New data sources and analytical techniques create opportunities for product innovation and improved business performance. Data solutions are developed with appropriate attention to performance, quality, maintainability, testing and documentation. Prototypes are translated into practical, production-ready solutions where business value is established. Internal teams and external clients receive clear technical guidance and effective analytical support. Collaboration across data, product and business teams contributes to successful project delivery. Work is prioritised effectively, estimates are realistic and deliverables meet agreed deadlines. Knowledge sharing and continuous improvement strengthen the team's technical capability and delivery standards. The position offers an opportunity for a technically strong, commercially aware data professional to contribute to innovative analytical products while developing expertise across data science, architecture and machine learning engineering