About This Role:
Our client is a leading luxury retail group in the Middle East, recognized for its commitment to excellence, sustainability, and social responsibility. The group oversees over 10 owned brands and partners with more than 400 international names across various luxury categories, earning the Great Place to Work certification in several markets. The organization pledges to reach Net Zero by 2040 and actively participates in global initiatives for women's empowerment and sustainable development.
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What You'll Be Doing:
You will design, build, and continuously improve machine learning models that facilitate predictive and intelligent decision-making across business functions. Your responsibilities will include ensuring the accuracy, fairness, and robustness of models through rigorous experimentation, tuning, and monitoring.
Key Accountabilities:
- Design and develop machine learning models for predictive, classification, or optimization use cases aligned with business needs.
- Oversee model training, tuning, and evaluation, using techniques to optimize performance, precision, and recall.
- Engineer and select relevant features to enhance model outcomes.
- Implement experiment tracking frameworks to ensure reproducibility and model comparability.
- Maintain model performance metrics such as accuracy, F1 score, and AUC.
- Develop strategies for detecting model drift and collaborate with MLOps and platform teams for retraining strategies.
- Ensure fairness and mitigate model bias through appropriate techniques.
- Collaborate with Data Engineers, MLOps, and Product Managers to integrate models into production.
- Document and version all models and experiments for governance and reuse.
- Apply generative AI techniques to support NLP and conversational use cases.
- Connect model outputs to Success KPIs quantifying value creation.
What You’ll Need To Succeed:
- 5–7 years of experience in applied data science or machine learning.
- Strong proficiency in Python and machine learning libraries (e.g., scikit-learn, XGBoost, LightGBM).
- Solid understanding of model development lifecycle, including training, tuning, evaluation, and deployment.
- Proven experience in feature engineering, statistical validation, and A/B testing.
- Knowledge of bias detection techniques and fairness frameworks.
Language Requirements:
No specific language requirements mentioned, but proficiency in English is typically expected in a professional setting.
