Senior Data Scientist
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Key skills for this role
About the Role
Chalhoub Group is seeking a Senior Data Scientist to design, build, and improve machine learning models for predictive and intelligent decision-making. The role requires 5-7 years of experience in applied data science, strong Python skills, and expertise in model development lifecycle, feature engineering, and MLOps.
Key Skills for This Role
Responsibilities
- Design and develop machine learning models for predictive, classification, or optimization use cases
- Own model training, tuning, and evaluation, using appropriate techniques to optimize performance
- Engineer and select relevant features, leveraging statistical and domain knowledge
- Implement experiment tracking frameworks to ensure reproducibility and model comparability
- Establish and maintain model performance metrics, including accuracy, F1 score, AUC, etc.
- Develop strategies for detecting model drift, including statistical monitoring of input data and prediction shifts
- Define retraining strategies and triggers based on performance degradation, data shifts, or periodic review cycles
- Ensure fairness and mitigate model bias, applying techniques to identify and reduce disparities
- Collaborate with Data Engineers, MLOps, and Product Managers to integrate models into production pipelines
- Document and version all models, experiments, and assumptions to support auditability, governance, and reuse
- Apply generative AI techniques, including LLM fine tuning, embeddings, and retrieval augmented generation
- Link model outputs to Success KPIs by quantifying value creation
Requirements
- 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: training, hyperparameter tuning, evaluation, deployment, monitoring
- Proven experience in feature engineering, including categorical encoding, normalization, and feature importance analysis
- Knowledge of model performance metrics, statistical validation, and A/B testing approaches
- Familiarity with bias detection techniques and fairness frameworks
- Experience with drift detection methods and retraining triggers
- Comfort working with structured and semi structured data, including tabular and time series formats
- Strong collaboration skills; able to work with MLOps, engineering, and product teams
- Working knowledge of MLOps practices (e.g., containerization, CI/CD, model serving)
- Hands on experience with Azure ML and GCP Vertex AI for model training, deployment, and monitoring
Full Job Posting
Company Overview
- Chalhoub Group is a partner and creator of luxury experiences in the Middle East for over seven decades.
- The Group has a portfolio of over 10 owned brands and represents over 400 international names across luxury categories.
- It operates over 950 stores, online platforms, and mobile apps, with a focus on omnichannel experiences.
- The Group has over 16,000 professionals across eight countries in the Middle East and LATAM.
What You'll Be Doing
- Design and develop machine learning models for predictive, classification, or optimization use cases.
- Own model training, tuning, and evaluation, using appropriate techniques to optimize performance.
- Engineer and select relevant features, leveraging statistical and domain knowledge.
- Implement experiment tracking frameworks to ensure reproducibility and model comparability.
- Establish and maintain model performance metrics, including accuracy, F1 score, AUC, etc.
- Develop strategies for detecting model drift, including statistical monitoring of input data and prediction shifts.
- Define retraining strategies and triggers based on performance degradation, data shifts, or periodic review cycles.
- Ensure fairness and mitigate model bias, applying techniques to identify and reduce disparities.
- Collaborate with Data Engineers, MLOps, and Product Managers to integrate models into production pipelines.
- Document and version all models, experiments, and assumptions to support auditability, governance, and reuse.
- Apply generative AI techniques, including LLM fine tuning, embeddings, and retrieval augmented generation.
- Link model outputs to Success KPIs by 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, machine learning libraries (e.g., scikit learn, XGBoost, LightGBM), and experimentation tools (e.g., MLflow, Weights & Biases).
- Solid understanding of model development lifecycle: training, hyperparameter tuning, evaluation, deployment, monitoring.
- Proven experience in feature engineering, including categorical encoding, normalization, and feature importance analysis.
- Knowledge of model performance metrics, statistical validation, and A/B testing approaches.
- Familiarity with bias detection techniques and fairness frameworks.
- Experience with drift detection methods (e.g., population stability index, data drift metrics) and retraining triggers.
- Comfort working with structured and semi structured data, including tabular and time series formats.
- Strong collaboration skills; able to work with MLOps, engineering, and product teams.
- Working knowledge of MLOps practices (e.g., containerization, CI/CD, model serving).
- Hands on experience with Azure ML and GCP Vertex AI for model training, deployment, and monitoring.
What We Can Offer You
- Enriching experiences, learning and development opportunities.
- Exposure to different assignments within your role or through internal mobility.
- Competitive benefits package including health care, child education contribution, remote and flexible working policies.
- Exclusive employee discounts.
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