Data Science

4 days ago

Roodepoort, Gauteng, South Africa CapCircle Management Consultants Full time R1 500 000 - R2 500 000 per year

Not for First Time Job Seeker

Important:
This role requires all 12 listed skills.

Please ensure your CV clearly reflects each of these with traceable experience and references. Candidates missing any of the required skills will not be considered.

JOB ROLE - DATA SCIENCE/ AI/ML Engineers:-

Educational qualifications

  • Bachelor's degree in data science, Statistics, Mathematics, Computer Science, or a related quantitative field.
  • Master's or PhD in Machine Learning, Applied Mathematics, Econometrics, or AI (strongly preferred).

SKILLS:

Machine learning engineering

Azure databricks

Business acumen

Python

SQL

Version control and collaboration tools (git, azure repo)

Strong communication with non-technical stakeholders

Cloud platform knowledge (azure)

Model deployment (CI/CD pipelines, model monitoring)

Understanding of rag pipelines and autonomous agents

Feature and ml data engineering (advanced big data wrangling)

Understanding of project management and agile methodologies

Relevant experience

  • 8–12 years of experience in data science, machine learning, or advanced analytics roles, with at least 3 years in a leadership or managerial capacity.
  • Proven delivery of AI/ML models in production environments—preferably in Fintech, Banking, or Telecoms.
  • Experience translating complex data science solutions into measurable business outcomes (e.g., credit scoring, fraud detection, churn prediction).
  • Exposure to cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI)."

Required certifications

Preferred: TensorFlow, AWS ML Speciality, Microsoft AI Fundamentals

Key responsibilities

  • Lead and scale a high-performing team of data scientists and ML engineers.
  • Define and execute the data science roadmap, aligned to business priorities and monetisation opportunities.
  • Oversee the end-to-end model lifecycle—from ideation, exploration, development, validation, to monitoring.
  • Partner with Product, Risk, and Engineering to integrate models into digital and operational channels.
  • Establish standards for model governance, explainability, and ethical AI practices.
  • Drive innovation by exploring emerging techniques in generative AI, reinforcement learning, and real-time scoring.

Work location

Hybrid

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