
Chalhoub Group
Senior Data Scientist
- Permanent
- Dubai, United Arab Emirates
- Experience 5 - 10 yrs
Job expiry date: 31/01/2026
Job overview
Date posted
17/12/2025
Location
Dubai, United Arab Emirates
Salary
AED 20,000 - 30,000 per month
Compensation
Comprehensive package
Experience
5 - 10 yrs
Seniority
Senior & Lead
Qualification
Bachelors degree
Expiration date
31/01/2026
Job description
The Senior Data Scientist at Chalhoub Group is responsible for designing, building, and continuously improving machine learning models that enable predictive, classification, optimization, and intelligent decision-making across multiple luxury retail business functions. The role focuses on ensuring model accuracy, robustness, fairness, and reliability through rigorous experimentation, feature engineering, hyperparameter tuning, evaluation, and continuous monitoring. The position involves implementing experiment tracking frameworks for reproducibility, defining and maintaining model performance metrics such as accuracy, F1 score, and AUC, detecting and mitigating model drift using statistical techniques including population stability index, and defining retraining strategies in collaboration with MLOps and platform teams. The Senior Data Scientist applies generative AI capabilities such as large language model fine-tuning, embeddings, and retrieval-augmented generation to support NLP and conversational use cases, while integrating models into production pipelines with Data Engineers, MLOps, and Product Managers. The role also requires documenting and versioning models and experiments for governance and auditability, linking model outputs to business success KPIs, and ensuring production readiness through MLOps practices using Azure ML and GCP Vertex AI within a large-scale omnichannel luxury retail ecosystem.
Required skills
Key responsibilities
- Design and develop machine learning models for predictive, classification, and optimization use cases aligned with business needs
- Own model training, hyperparameter tuning, and evaluation to optimize performance, precision, recall, and robustness
- Engineer and select features using statistical methods and domain knowledge to improve model outcomes
- Implement experiment tracking frameworks to ensure reproducibility and comparability across model versions
- Establish and monitor model performance metrics including accuracy, F1 score, AUC, and other relevant indicators
- Develop and apply model drift detection strategies using statistical monitoring of data and prediction shifts
- Define retraining strategies and triggers based on data drift, performance degradation, or review cycles in collaboration with MLOps teams
- Ensure fairness and mitigate model bias using bias detection techniques and fairness frameworks
- Collaborate with Data Engineers, MLOps, and Product Managers to integrate models into scalable production pipelines
- Document and version models, experiments, assumptions, and artifacts to support governance, auditability, and reuse
- Apply generative AI techniques including LLM fine-tuning, embeddings, and retrieval-augmented generation for NLP and conversational use cases
- Quantify business impact by linking model outputs to success KPIs such as time savings, accuracy gains, or revenue impact
Experience & skills
- Possess 5–7 years of experience in applied data science or machine learning
- Demonstrate strong proficiency in Python and machine learning libraries including scikit-learn, XGBoost, and LightGBM
- Have experience with experimentation and tracking tools such as MLflow and Weights & Biases
- Exhibit solid understanding of the full model development lifecycle including training, tuning, deployment, monitoring, and retraining
- Demonstrate hands-on experience in feature engineering, statistical validation, and A/B testing
- Show familiarity with bias detection, fairness frameworks, and drift detection methods such as population stability index
- Have experience working with structured and semi-structured data including tabular and time-series formats
- Demonstrate working knowledge of MLOps practices including containerization, CI/CD, and model serving
- Show hands-on experience with Azure ML and GCP Vertex AI for model training, deployment, and monitoring