About the position
We are seeking a Data Scientist to join our team in Cape Town, onsite.
The Data Scientist will enhance, maintain, and evolve the analytical and optimisation models used to drive product assortment decisions—including substitution logic, leftover-stock (“carcass”) balancing, and fraud-related anomaly detection.
This is a highly analytical, process-driven role suited to someone with a strong quantitative foundation, ideally with engineering or operational research training, and a mindset geared toward optimisation, efficiency, and continual improvement.
The team values sharp talent—individuals who are switched on, curious, and intellectually agile.
Key Responsibilities
Store Clustering & Assortment Optimisation
- Enhance and maintain clustering models that group stores based on department-level similarities.
- Analyse department attributes across stores to determine optimal product allocations.
- Develop data-driven insights into customer needs to improve assortment effectiveness.
- Substitution Logic & Customer Need Modelling
- Build and refine models that calculate substitution scores—identifying alternative products when preferred items are unavailable.
- Support decision-making about which substitutes can be routed to which stores based on demand patterns.
Carcass Balancing (Leftover Stock Optimisation)
- Analyse leftover stock and determine optimal redistribution strategies across stores.
- Build optimisation algorithms to minimise waste and maximise stock clearance efficiency.
Audit, Controls & Fraud Detection
- Identify outliers, unusual patterns, and potential fraud within assortment and clustering processes.
- Contribute to automated audit mechanisms and anomaly detection models.
Model Improvement & Operational Excellence
- Work with complex existing models and enhance them for improved accuracy and speed.
- Apply engineering thinking to streamline processes, improve throughput, and increase operational reliability.
- Work closely with the business to validate outputs and translate technical findings into practical [URL Removed] Collaboration
- Partner with teams across merchandising, supply chain, planning, and analytics.
- Communicate insights clearly to both technical and non-technical stakeholders.
- Participate in experimentation, A/B testing, and continuous improvement cycles.
- Qualifications
- Bachelor’s or master’s degree in:
- Engineering (Industrial, Systems, Operations, etc.) – strongly preferred
- Operational Research
- Data Science, Mathematics, Statistics, or equivalent quantitative field
Experience & Skills
- 3+ years’ experience in data science, optimisation, modelling, or operations analytics (mid-level)
OR - 5–7+ years for senior candidates
- Strong experience with:
- Python or R
- SQL
- Machine Learning modelling
- Optimisation frameworks (e.g., linear programming, constraint optimisation)
- Experience working with complex, production-level models (experience with consulting-style modelling advantageous).
- Ability to understand business processes and translate them into analytical solutions.
- Strong problem-solving and conceptual thinking abilities.
- Exceptional analytical curiosity—someone who is switched on, sharp, and comfortable working in a fast-paced environment.
Competencies
- Strong business acumen and the ability to understand store operations.
- High attention to detail and accuracy.
- Ability to work with complex datasets and ambiguous problems.
- Agile, proactive, and intellectually quick.
- Excellent communication, particularly when explaining modelling rationale to stakeholders.
- Passion for optimisation, efficiency, and continuous improvement.
Desired Skills:
