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.
Requirements
To qualify for this position, you need:
- 3–5 years of hands-on experience in a data science or applied machine learning role.
- Strong proficiency in Python for data science (NumPy, pandas, scikit-learn, PyTorch or TensorFlow).
- 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 (LangChain, LlamaIndex, OpenAI API, Claude, Hugging Face).
- Experience building RAG patterns with vector stores (e.g. PostgreSQL/pgvector).
- Exposure to computer vision frameworks (OpenCV, torchvision, YOLO, Detectron2).
- Familiarity with AI automation tools (n8n, Zapier, Base44) and AI dev tools (Claude Code).
- Front-end skills (React, TypeScript, Tailwind, Framer Motion) for building model-facing tools.
- Experience integrating with Zoho CRM (Deluge, JavaScript)
- Postgraduate degree (Honours, Masters, or PhD) in a quantitative field such as Computer Science, Statistics, Mathematics, or Engineering.
Duties and responsibilities include, but not limited to:
Machine Learning & Predictive Modelling
- 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.
NLP & Generative AI
- 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.
Computer Vision
- 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.
Analytics, Dashboards & Statistical Insights
- 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, TypeScript, Tailwind, and Framer Motion to track model and business KPIs.
AI Automation & Workflow Integration
- Use AI developer tools such as Claude Code and LLMs to accelerate experimentation and delivery.
- Automate data and model workflows with n8n, Zapier, and Base44.
- Integrate model outputs and insights into iGrow systems including Zoho CRM (via Deluge and APIs).
Collaboration & Research
- 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.
Desired Skills:
- Python
- ML fundamentals
- NLP
- SQL
- PostgreSQL
- AI tooling
Desired Qualification Level:
- Degree
About The Employer: