Data Scientist, Sasol
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Sandton, South Africa· 5 days agoAbout Sasol
Sasol 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 Data Scientist is responsible for the development, operationalisation and modification of data science products in accordance with sound analytical principles and standards to deliver business value and enable data-driven business decisions.
The role works closely with Senior Data Scientists and business technical teams to generate business insights, predictions and recommendations. The Data Scientist will apply recent techniques and research to propose new approaches and methodologies for business opportunities, including applied AI techniques and data-driven automation where appropriate.
The role will lead less-complex projects while supporting larger projects under the guidance of Senior Data Scientists.
Responsibilities
- Lead and support the end-to-end development of Data Science products in accordance with sound analytical principles and data science standards.
- Lead less-complex data science projects and support larger projects under the guidance of Senior Data Scientists.
- Apply Data Science knowledge across Advanced Analytics, Machine Learning and Simulation.
- Maintain general awareness across different areas of Data Science and develop basic exposure to modern AI techniques, including NLP, computer vision and generative AI concepts.
- Apply modern data science programming languages and platforms, including Python and SQL.
- Develop exposure to cloud-based analytics platforms such as Azure and Databricks and emerging AI frameworks.
- Apply version control practices to manage code, analytical artefacts and model iterations using tools such as Git and MLFlow.
- Ensure solutions are secure, sustainable and appropriately packaged for support handover.
- Maintain basic documentation and demonstrate awareness of deployment and operational considerations.
- Work across multiple projects using Agile and/or Waterfall execution methods.
- Keep up to date with trends, best practices and technologies to distinguish between complex problem types and appropriate modelling approaches.
- Apply an awareness of responsible and ethical AI practices.
- Proactively identify real business needs and opportunities where Data Science support can add value.
- Clearly explain technical concepts, analytical approaches and model outputs to non-technical business stakeholders in a structured and actionable manner.
Minimum Qualifications and Experience & Knowledge
- 4-year University Bachelor’s Degree in Data Science, Engineering, Computer Science, Mathematics or a related field.
- Postgraduate qualification in a quantitative or analytical field will be advantageous.
- Minimum 3 years of relevant experience.
- Exposure to relevant Data Science, cloud or applied AI certifications will be advantageous.
- Experience with modern Data Science programming languages and platforms, particularly Python and SQL.
- Exposure to cloud-based analytics platforms such as Azure and Databricks.
- Exposure to modern AI techniques, including NLP, computer vision or generative AI concepts.
- Familiarity with version control and analytical/model management tools such as Git and MLFlow.
Operational responsibility (Internal process)
- Develop, operationalise and modify Data Science products according to established analytical principles and standards.
- Apply appropriate modelling approaches to business problems.
- Ensure data science solutions are secure, sustainable and appropriately documented.
- Prepare solutions for effective support handover.
- Manage analytical artefacts, code and model iterations using appropriate version control practices.
- Apply Agile and/or Waterfall methodologies across assigned projects.
- Consider responsible and ethical AI principles throughout the development and application of data science solutions.
- Identify opportunities for data-driven and AI-enabled process improvement.
People Management/Self-Management
- Work collaboratively with Senior Data Scientists, technical teams and business stakeholders.
- Build productive and trusting relationships with internal and external stakeholders.
- Clearly communicate technical concepts, analytical approaches and model outputs to non-technical audiences.
- Demonstrate customer focus by understanding and responding to business needs.
- Take ownership of assigned projects and contribute effectively to larger initiatives.
- Demonstrate continuous learning and awareness of emerging Data Science, AI and cloud technologies.
- Support effective collaboration across engineering and digital teams.
Financial controls and planning
- Apply data science and analytical solutions to support data-driven business decisions.
- Identify opportunities to improve the effectiveness and efficiency of business processes through automation and data-driven approaches.
- Consider business value and customer needs when developing data science solutions.
- Support solutions that contribute to improved business performance and decision-making.
Key Competencies
- Business Acumen: Understands how the business operates, including profitability drivers and the broader market context.
- Compliance: Understands relevant rules, regulations, statutory requirements and guidelines, including data privacy and responsible AI principles.
- Continuous Process Improvement: Identifies opportunities to improve business processes through automation, data-driven and AI-enabled approaches.
- Customer Focus: Maintains focus on customer needs and responds effectively to requirements.
- Data Management: Understands organisational data management, administration and relationships.
- Decision Making: Makes timely and effective decisions in a fast-paced and changing environment.
- Negotiating: Works cooperatively to reach mutually satisfactory outcomes.
- Partnership Leadership: Builds relationships and partnerships across organisational boundaries, including with engineering and digital teams.
- Relationship Management: Develops and manages long-term, trusting relationships with stakeholders.
- Reporting: Accesses information from databases and other sources and prepares reports according to requirements.
Interested candidates should join the Sasol Customer Value Recruitment Challenge.