Data Scientist Qualification & Experience Minimum Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. 35 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 Masters 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

What you will be doing: Extracting and analyzing digital, clinical and behavioral data to inform product design, customer experience strategies for digital solutions. Developing data science model and A/B testing pipelines for deployment. Applying statistical techniques, behavioral science principles, and data science models to measure and optimize member engagement and user experiences for digital solutions. Working closely with Organisation Health Digital to ensure insights and modelling inputs align with product objectives and app engagement outcomes. Collaborate with systems and engineering teams to integrate data science pipelines into the Health Systems and Health digital solutions. Present insights and results to business stakeholders and senior executives, highlighting the impact of personalized digital experiences on user engagement and experience. What we are looking for : BSc degree in Mathematics, Statistics, Computer Science, or related fields with a minimum average of 65% across all subjects. 5 years’ experience as a Data Scientist. Ability to formulate a clear problem statement, develop a plan for tackling it, and clearly communicate findings verbally, visually, and in writing  Demonstrable working experience in an analytics position, where the focus was on building and implementing machine learning models to solve business problems Experience accessing and analysing data using language/tools/databases such as Python, R, SQL, etc. Experience using Gradient Boosting Machines, Random Forests, Neural Networks or similar algorithms. Some experience in working with big disparate sets of data and exposure to big data tools  Please note that if you do not hear from us within 3 weeks, consider your application unsuccessful. Â

What you will be doing: Extracting and analysing digital, clinical and behavioural data to inform product design, customer experience strategies for digital solutions. Developing data science model and A/B testing pipelines for deployment. Applying statistical techniques, behavioural science principles, and data science models to measure and optimize member engagement and user experiences for digital solutions. Working closely with Organisation Health Digital to ensure insights and modelling inputs align with product objectives and app engagement outcomes. Collaborate with systems and engineering teams to integrate data science pipelines into the Health Systems and Health digital solutions. Present insights and results to business stakeholders and senior executives, highlighting the impact of personalized digital experiences on user engagement and experience. What we are looking for : BSc degree in Mathematics, Statistics, Computer Science, or related fields with a minimum average of 65% across all subjects. 5 years experience as a Data Scientist. Ability to formulate a clear problem statement, develop a plan for tackling it, and clearly communicate findings verbally, visually, and in writing Demonstrable working experience in an analytics position, where the focus was on building and implementing machine learning models to solve business problems Experience accessing and analysing data using language/tools/databases such as Python, R, SQL, etc. Experience using Gradient Boosting Machines, Random Forests, Neural Networks or similar algorithms. Some experience in working with big disparate sets of data and exposure to big data tools Please note that if you do not hear from us within 3 weeks, consider your application unsuccessful.

Qualifications: Bachelors Degree in sciences or engineering with a strong focus on computer science, statistics, mathematics, and/or actuarial sciences Fluency in Python, SQL, and R Beneficial if fluent in any of the following programming languages (C#.NET, C, C++, Visual Basic, SQL) Experience: 3-5 years experience in data warehousing, data sciences, and/ or modelling environment Good understanding of software languages and software infrastructure Experience in working with and analyzing data Insurance Industry Experience and Actuarial background Statistical and Mathematical Skills: Predictive modelling skills Knowledge of machine learning principles at a high level and the ability to implement as and when needed Critical analytical thinking and attention to detail Programming and high-level understanding of cloud and web infrastructure: R, SQL, Python, and JavaScript experience High level of competency required in Python and SQL Beneficial to have experience deploying web applications to Azure Spark experience Version control principles

In this role, you will develop and deploy machine learning and statistical models, design AI-driven solutions, and tackle complex analytical challenges. You will work across the entire data lifecycle – from data discovery to insight delivery – while leveraging modern platforms like AWS and SAS Viya to build scalable, high-impact solutions. This position is perfect for someone who combines deep technical expertise with strong business understanding. Key Responsibilities: Manage full data workflows: data extraction, cleaning, preparation, and analysis Create meaningful dashboards and visualisations to communicate insights Build and deploy statistical and machine learning models using SAS, Python, and SQL Develop new AI-driven solutions for complex business problems Translate ambiguous or unstructured business challenges into clear analytical approaches Work within AWS and SAS Viya environments for development and deployment Collaborate closely with business teams to understand needs before building solutions Present findings and recommendations in a clear, concise manner to stakeholders Job Experience and Skills Required: Education: Degree in quantitative field such as Applied Mathematics, Statistics, Data Science, or related disciplines Strong theoretical grounding in statistical modelling is essential Experience: Strong background in data preparation, data wrangling, and exploratory analysis Experience working with SAS, Python, and SQL in production environments Proven experience with machine learning or statistical models Exposure to cloud technologies, preferably AWS Experience using data visualisation tools (e.g., SAS Visual Analytics or similar) Skills Strong analytical and problem-solving ability Ability to work independently and take full ownership of solutions Excellent communication skills and the ability to tell a clear data-driven story High levels of curiosity and willingness to experiment and learn Strong collaboration skills able to work closely with both technical and non-technical teams Enjoys tackling challenging problems and thrives in a dynamic environment For more exciting Actuarial and Analytics vacancies, please visit: