Advanced Analytics & AI Solutions Engineer

SAB; Sandton; On-site; ZAR 900,000 - 1,500,000; Full time

Job summary

SAB in Sandton seeks a data science professional to develop predictive analytics, ML use cases, and production-ready analytical solutions for local commercial priorities.

You will build interactive HTML visualisations and automation tools, translate commercial problems into technical requirements, and own the solution lifecycle from development to deployment and monitoring. Collaboration with data science and commercial stakeholders is essential.

Qualifications

• Bachelor's degree in a relevant field (data science, CS, engineering, IT).
• 2+ years in advanced analytics, data science or software development.
• Strong Python and SQL with knowledge of PySpark and Databricks.
• Experience building analytical apps and HTML visualisations.
• Ability to translate technical concepts into business language.

Responsibilities

• Develop predictive analytics, ML use cases and statistical solutions.
• Build analytical apps, automation tools and HTML visualisations.
• Translate commercial problems into technical requirements and prototypes.
• Explore large datasets to find patterns and opportunities.
• Own full solution lifecycle: build, deploy, monitor, maintain, enhance.
• Write reusable, well-documented code and standards.
• Collaborate with Data Scientist, Commercial stakeholders and Data Tech teams.
• Support user adoption and troubleshoot production issues

Skills

Python

SQL

Machine Learning

Data Analysis

Education

Bachelor's degree in Data Science, Computer Science, Mathematics, Engineering, Information Technology or related field

Tools

PySpark

Databricks

Power Apps

Streamlit

Web application frameworks

Job description

SAB in Sandton seeks a data science professional to develop predictive analytics, ML use cases, and production-ready analytical solutions for local commercial priorities.

You will build interactive HTML visualisations and automation tools, translate commercial problems into technical requirements, and own the solution lifecycle from development to deployment and monitoring. Collaboration with data science and commercial stakeholders is essential.