Senior Data Scientist, Sasol
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Sandton, South Africa· 5 days agoSasol is a global energy and chemicals company serving customers through an extensive retail and commercial network. Understanding customer behaviour and future value is central to delivering personalised experiences and sustainable business growth. In this challenge, participants will apply data science to predict future Customer Lifetime Value (CLV) using real-world customer data. Beyond competing for leaderboard success, exceptional performers may also have the opportunity to explore future Data Scientist and Senior Data Scientist opportunities with Sasol
Purpose of the role
- The Senior Data Scientist will be responsible for preparing and analysing large datasets and applying advanced data science techniques, including statistics, simulation, optimisation and machine learning, to drive predictive and prescriptive intelligence across Sasol’s Retail and B2B channels in South Africa.
- The role involves the design, engineering, development, operationalisation and continuous enhancement of complex data science products that support critical, value-adding business decisions.
- The role also includes developing advanced AI solutions, including agentic AI applications capable of reasoning, retrieving information, calling tools and executing multi-step workflows within governed enterprise environments.
- The Senior Data Scientist will build strong relationships with stakeholders to understand business needs, maximise solution adoption and deliver measurable business value.
Responsibilities
- Translate business questions into clear quantitative problems using appropriate data science techniques, including statistics, simulation, optimisation and machine learning.
- Translate complex business opportunities into production-ready AI and agentic AI solutions using natural language interaction, reasoning, retrieval and tool use.
- Design secure, governed and responsible AI solutions, considering data privacy, access control, model risk and human-in-the-loop controls.
- Define, apply and maintain Data Science and AI best practices and standards.
- Ensure data science and AI solutions are sustainable and appropriately packaged for support handover.
- Peer review the work of junior data scientists to improve efficiency, effectiveness, quality and accuracy.
- Provide technical guidance on software engineering practices, deployment readiness and scalable AI solution design.
- Clearly communicate analytical, machine learning and agentic AI concepts to business stakeholders, product owners and technical teams.
- Proactively identify opportunities where data science and AI can create additional business value.
- Research emerging data science tools, techniques and frameworks for building, evaluating, deploying and monitoring agentic AI systems.
- Contribute to the data science community through knowledge sharing and subject-matter expertise.
- Mentor junior data scientists and provide technical leadership on AI solution design, code quality, deployment practices and responsible AI use.
Minimum Qualifications and Experience & Knowledge
- Minimum 4-year Bachelor’s degree in Statistics, Mathematics, Data Science, Operations Research, Engineering, Computer Science or a related field.
- A relevant Master’s degree is preferred.
- At least 6 years of applied experience in advanced analytics, statistical modelling and data science delivery across complex predictive business problems.
- Strong stakeholder communication and business engagement skills.
- Advanced coding capability in Python and SQL.
- Proven ability to develop robust, maintainable and production-ready analytical and AI applications.
- Hands-on experience building enterprise AI solutions using large language models.
- Experience with retrieval-augmented generation (RAG), tool-calling and agentic workflows.
- Experience designing and orchestrating AI/agent systems involving multi-step workflows, tool integration and multi-agent coordination.
- Experience integrating AI systems with enterprise data sources, APIs and vector stores within governed environments.
- Strong experience productionising AI solutions using cloud platforms such as Azure and Databricks.
- Experience with distributed computing, Git, CI/CD and API/service-based architectures.
- Experience evaluating, monitoring and optimising AI systems, including observability, performance, cost and quality.
- Experience developing and integrating AI capabilities into user-facing applications, including APIs, web applications and lightweight front-ends.
Operational responsibility (Internal process)
- Translate complex business needs into analytical, machine learning and AI solutions.
- Develop and operationalise production-ready data science and AI products.
- Apply appropriate data science and AI standards throughout solution development.
- Ensure solutions are secure, governed, maintainable and suitable for enterprise deployment.
- Evaluate and continuously improve the performance, quality and cost of AI systems.
- Ensure completed solutions are appropriately packaged and prepared for support handover.
- Apply responsible AI principles across the design and deployment lifecycle.
People Management/Self-Management
- Mentor and provide technical guidance to junior data scientists.
- Peer review analytical and AI work to improve quality and accuracy.
- Share knowledge and contribute to the data science community.
- Work effectively across business, engineering, platform and product teams.
- Take accountability for personal growth through continuous learning, feedback and self-development.
- Demonstrate strong problem-solving, critical reasoning and execution capabilities.
- Stay current with emerging enterprise AI technologies, tools and deployment patterns.
Financial controls and planning
- Develop data science and AI solutions that create measurable business value.
- Consider performance, scalability and cost when designing and deploying AI systems.
- Contribute to the optimisation of AI system performance and operational costs.
- Apply appropriate governance, risk management and responsible AI practices when developing enterprise solutions.
Key Competencies
- Analytics: Applies advanced analytical and AI reasoning approaches to solve complex business problems.
- Collaboration: Works effectively across business, engineering, platform and product teams.
- Critical Reasoning: Evaluates when agentic AI is and is not an appropriate solution pattern.
- Data Management: Understands structured and unstructured data required for modern AI retrieval and grounding.
- Execution Capability: Delivers AI solutions from concept through deployment, monitoring and continuous improvement.
- Problem Solving: Uses structured analysis, critical thinking and persistence to solve complex problems.
- Self-Mastery: Takes accountability for continuous professional development.
- Tech Savvy: Maintains practical awareness of emerging enterprise AI technologies and industry practices.
Interested candidates should join the Sasol Customer Value Recruitment Challenge.
- Company
- Sasol
- Employment Type
- Full-time
- Experience
- More than 5 years
- Location
- South Africa, Sandton