Data Scientist

Cape Town, South Africa | Posted on 29/06/2026

This is a compelling opportunity to join a market leader where you will work at the intersection of data, machine learning, and business impact, using advanced analytics to drive strategic decision‑making, enhance performance, and contribute to the future of financial innovation and value creation in South Africa.

To qualify for this position, you need:

• 3–5 years of hands‑off experience in a data science or applied machine learning role.
• Strong proficiency in Python for data science (Num Py, pandas, scikit‑learn, PyTorch or Tensor Flow).
• Solid understanding of ML fundamentals: model selection, cross‑validation, regularisation, and evaluation metrics.
• Practical experience with NLP and language models (transformers, BERT, GPT-family, etc.).
• Proficiency in SQL and working with PostgreSQL for data extraction and manipulation.
• Experience deploying models with Docker.
• Strong statistical foundations: hypothesis testing, probability, regression, and experimental design.
• Ability to communicate technical work clearly to non‑technical audiences.
• Experience with generative AI tooling (Lang Chain, Llama Index, OpenAI API, Claude, Hugging Face).
• Experience building RAG patterns with vector stores (e.g., PostgreSQL/pgvector).
• Exposure to computer vision frameworks (OpenCV, torch vision, YOLO, Detectron2).
• Familiarity with AI automation tools (n8n, Zapier, Base
  44) and AI dev tools (Claude Code).
• Front‑end skills (React, Type Script, Tailwind, Framer Motion) for building model‑facing tools.
• Postgraduate degree (Honours, Masters, or PhD) in a quantitative field such as Computer Science, Statistics, Mathematics, or Engineering.

• Design, train, evaluate, and deploy supervised and unsupervised machine learning models in Python.
• Build predictive and prescriptive models across classification, regression, clustering, and ranking tasks.
• Own the full ML lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring.
• Package and deploy models in Docker for reproducible, versioned, maintainable production use.

• Develop and fine‑tune NLP models for text classification, named entity recognition, sentiment analysis, and summarisation.
• Leverage LLMs and generative AI (Claude, OpenAI API, Hugging Face) to build intelligent applications.
• Design prompt engineering strategies and retrieval‑augmented generation (RAG) pipelines using PostgreSQL/pgvector for vector storage.
• Evaluate and mitigate risks in generative AI outputs including hallucination, bias, and fairness.

• Build and adapt computer vision models for image classification, object detection, and segmentation.
• Work with pre‑trained architectures (e.g. CNNs, ViTs) and fine‑tune on domain‑specific datasets.
• Collaborate with engineering teams to integrate vision models into production systems.

• Conduct rigorous exploratory data analysis (EDA) and statistical modelling to surface actionable insights.
• Design and analyse A/B tests and experiments to measure the impact of product and business changes.
• Translate complex analytical findings into clear, compelling narratives for non‑technical stakeholders.
• Build dashboards and internal tools using React, Type Script, Tailwind, and Framer Motion to track model and business KPIs.

• Use AI developer tools such as Claude Code and LLMs to accelerate experimentation and delivery.
• Automate data and model workflows with n8n, Zapier, and Base
  44.
• Integrate model outputs and insights into iGrow systems including Zoho CRM (via Deluge and APIs).
• Partner with data engineers to ensure high‑quality data is available for modelling.
• Work with product managers and stakeholders to define problems, success criteria, and evaluation metrics.
• Stay current with research developments in ML, NLP, and AI; evaluate and apply relevant techniques.
• Document methodologies, experiments, and model decisions to support reproducibility and knowledge sharing.