Responsibilities: 

The purpose: To analyse large volumes of structured and unstructured data, develop predictive and classification models, and create data-driven solutions aligned with business needs. The role also includes preparing and transforming data for modelling, deploying machine learning models, and collaborating across teams to drive innovation and deliver actionable insights. 

Data Analysis and Modelling

• Analyse large volumes of structured and unstructured data to identify trends, patterns, and actionable insights.

• Develop, train, and validate predictive and classification models such as regression, decision trees, clustering, and recommendation systems.

• Work with time-series, transactional, and behavioural datasets to generate advanced analytical insights.

Model Development and Deployment

• Implement machine learning models using Python, R, or similar programming languages.

• Collaborate with engineering teams to deploy and integrate models into production systems and applications.

• Continuously monitor and fine-tune deployed models to ensure optimal performance and accuracy.

Data Preparation and Feature Engineering

• Partner with data engineers to define, extract, and structure relevant datasets.

• Clean, transform, and normalize raw data to enable accurate modelling.

• Perform feature extraction and conduct exploratory data analysis (EDA) to inform modelling and reporting activities.

Business Alignment

• Align data science initiatives with business goals to drive measurable impact.

• Translate complex analytical outputs into clear, actionable recommendations for stakeholders.

• Collaborate cross-functionally to ensure data-driven solutions meet operational and strategic needs.

Business Collaboration and Communication

• Collaborate with business stakeholders to understand key challenges and identify data-driven opportunities.

• Translate complex business problems into data science solutions with clear objectives and measurable outcomes.

• Communicate analytical findings effectively to non-technical audiences through dashboards, visualizations, and reports.

Collaboration and Best Practices

• Work within cross-functional Agile teams to deliver iterative, high-impact data science solutions.

• Document methodologies, model logic, and code to support knowledge sharing and team reusability.

• Contribute to the evolution and improvement of data science standards, frameworks, and best practices.

Requirements: 

• Bachelors or Honours degree in Data Science, Statistics, Computer Science, Applied
  Mathematics, or related field.
• Minimum applicable experience (years): 35 years of experience in a data science role, ideally in a fast–paced or product–led environment
• Proficient in Python (preferred) or R for data science and machine learning tasks.
  Experience with Azure Fabric ML libraries .
• Solid SQL skills; familiarity with big data tools (e.g., Azure Synapse Analytics, Fabric) is a
  plus.
• Experience with Power BI as primary data visualization tool.
• Experience working with APIs, Azure cloud environments , and Git–based workflows

How to Apply: 

Kindly forward your CV to Gaby Turner at *****@*****.co.za or you may forward your CV to *****@*****.co.za