About the position
Data Scientist
Qualification & Experience
Minimum
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- 3–5 years of experience in data science, analytics, or a related field.
- Proven experience with machine learning, predictive modelling, and statistical analysis.
- Strong proficiency in Python, R, SQL, and data visualisation tools (e.g., Power BI, Tableau).
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is advantageous.
- Familiar with version control systems (e.g., Git) and collaborative development practices.
Advantageous
- Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Experience in healthcare, retail, or insurance data ecosystems
Organogram
Objective/Purpose
The Data Scientist is responsible for leveraging advanced analytics, machine learning, and statistical modelling to extract actionable insights from complex datasets. This role supports strategic decision-making, drives innovation, and enhances operational efficiency across the organisation.
Key Performance Areas
Advanced Data Analysis & Modelling
- Develop, implement, and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes, enabling proactive decision-making and strategic planning.
- Analyse large and complex datasets using statistical techniques to uncover patterns and trends, driving data-informed insights and operational improvements.
- Monitor model performance using validation metrics and retrain models as needed to maintain accuracy, ensuring continued relevance and reliability of outputs.
- Translate business challenges into analytical problems using structured frameworks, enabling the development of targeted and effective data solutions.
Data Engineering & Management
- Collaborate with data engineers to build robust data pipelines and ensure data integrity.
- Maintain and optimize data storage solutions for scalability and performance.
- Identify opportunities for automation in reporting and analysis using scripting and APIs, increasing efficiency, and reducing turnaround time.
- Document methodologies, assumptions, and outcomes in a clear and reproducible format to support transparency, governance, and knowledge sharing.
Business Intelligence & Strategic Insights
- Translate complex data into actionable insights that support strategic decision-making.
- Identify trends, patterns, and anomalies that inform business strategies and operational improvements.
- Develop and maintain dashboards and reports for various business units.
Solution Development & Deployment
- Build end-to-end data science solutions, from prototype to production.
- Integrate models into business applications or platforms using APIs or other deployment methods.
- Monitor deployed models for performance drift and retrain as necessary
Stakeholder Engagement & Communication
- Work closely with business stakeholders to understand requirements and define analytical approaches.
- Communicate findings clearly through presentations, visualisations, and storytelling to enhance stakeholder understanding and engagement.
- Provide training and support to non-technical users on data tools and insights to build analytical capacity, empowering teams to leverage data independently.
Innovation & Continuous Improvement
- Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.
- Contribute to the development of best practices, standards, and frameworks within the data science team to ensure consistency and quality.
Governance, Compliance & Ethical Use of Data
- Ensure compliance with data privacy regulations by applying ethical data managing practices, protecting sensitive information, and maintaining stakeholder trust.
- Implement model governance practices including documentation, versioning, and audit trails.
- Apply bias mitigation techniques in model development to ensure fairness, accuracy, and responsible AI practices
Role Competencies
Technical
- Deep understanding of statistical methods, probability theory, linear algebra, and calculus to support model development and data interpretation.
- Advanced proficiency in Python, R, SQL, and familiarity with Java or Scala. Ability to write clean, efficient, and reusable code.
- Experience with supervised and unsupervised learning, deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluation techniques.
- Knowledge of data warehousing, ETL processes, and working with structured and unstructured data.
- Familiar with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and scalable data solutions.
- Skilled in using tools like Power BI, Tableau
Analytical & Problem-Solving Skills
- Ability to approach problems logically, identify root causes, and propose data-driven solutions.
- Understands business operations and can align data science initiatives with strategic goals.
- Continuously seeks new methods, tools, and approaches to improve analytical outcomes and business impact.
Communication & Influence
- Capable of translating complex data findings into clear, compelling narratives for diverse audiences.
- Builds strong relationships with internal and external stakeholders, understands their needs, and delivers relevant insights.
- Confident in presenting technical content to non-technical audiences, including executives and decision-makers.
Collaboration & Teamwork
- Works effectively with product managers, engineers, analysts, and business leaders to co-create solutions.
- Comfortable working in iterative environments, adapting to changing priorities and feedback.
Adaptability and Agility
- Demonstrates the ability to navigate ambiguity with confidence and composure.
- Adapts effectively to shifting priorities, evolving goals, and dynamic business contexts.
- Contributes proactively to refining processes, structures, and ways of working to support organisational growth.
- Brings strong problem-solving skills, flexibility, and resilience, coupled with a learning and growth mindset, to thrive in an agile, high-growth environment.
Special Conditions of Employment
Working conditions
This role follows a hybrid work model, allowing flexibility in where you work while requiring in-person presence when operational needs arise.
Legal Requirements
South African citizen
MIE, no criminal record and clear credit rating
Data Scientist
Qualification & Experience
Minimum
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- 3–5 years of experience in data science, analytics, or a related field.
- Proven experience with machine learning, predictive modelling, and statistical analysis.
- Strong proficiency in Python, R, SQL, and data visualisation tools (e.g., Power BI, Tableau).
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is advantageous.
- Familiar with version control systems (e.g., Git) and collaborative development practices.
Advantageous
- Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Experience in healthcare, retail, or insurance data ecosystems
Organogram
Objective/Purpose
The Data Scientist is responsible for leveraging advanced analytics, machine learning, and statistical modelling to extract actionable insights from complex datasets. This role supports strategic decision-making, drives innovation, and enhances operational efficiency across the organisation.
Key Performance Areas
Advanced Data Analysis & Modelling
- Develop, implement, and maintain predictive and prescriptive models using machine learning algorithms to forecast business outcomes, enabling proactive decision-making and strategic planning.
- Analyse large and complex datasets using statistical techniques to uncover patterns and trends, driving data-informed insights and operational improvements.
- Monitor model performance using validation metrics and retrain models as needed to maintain accuracy, ensuring continued relevance and reliability of outputs.
- Translate business challenges into analytical problems using structured frameworks, enabling the development of targeted and effective data solutions.
Data Engineering & Management
- Collaborate with data engineers to build robust data pipelines and ensure data integrity.
- Maintain and optimize data storage solutions for scalability and performance.
- Identify opportunities for automation in reporting and analysis using scripting and APIs, increasing efficiency, and reducing turnaround time.
- Document methodologies, assumptions, and outcomes in a clear and reproducible format to support transparency, governance, and knowledge sharing.
Business Intelligence & Strategic Insights
- Translate complex data into actionable insights that support strategic decision-making.
- Identify trends, patterns, and anomalies that inform business strategies and operational improvements.
- Develop and maintain dashboards and reports for various business units.
Solution Development & Deployment
- Build end-to-end data science solutions, from prototype to production.
- Integrate models into business applications or platforms using APIs or other deployment methods.
- Monitor deployed models for performance drift and retrain as necessary
Stakeholder Engagement & Communication
- Work closely with business stakeholders to understand requirements and define analytical approaches.
- Communicate findings clearly through presentations, visualisations, and storytelling to enhance stakeholder understanding and engagement.
- Provide training and support to non-technical users on data tools and insights to build analytical capacity, empowering teams to leverage data independently.
Innovation & Continuous Improvement
- Experiment with new techniques to improve model performance and analytical capabilities fostering innovation and continuous improvement.
- Contribute to the development of best practices, standards, and frameworks within the data science
