Senior Data Scientist
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Key skills for this role
About the Role
Chalhoub Group is looking for a Senior Data Scientist to design and improve machine learning models for predictive decision-making in luxury retail. The role requires 5-7 years of experience, strong Python skills, and expertise in ML lifecycle, feature engineering, and MLOps.
Key Skills for This Role
Responsibilities
- Design and develop machine learning models for predictive, classification, or optimization use cases aligned to business needs
- Own model training, tuning, and evaluation, using appropriate techniques to optimize performance, precision, and recall
- Engineer and select relevant features, leveraging statistical and domain knowledge to improve model outcomes
- Implement experiment tracking frameworks to ensure reproducibility and model comparability across versions
- Establish and maintain model performance metrics, including accuracy, F1 score, AUC, etc., depending on use case
- 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, in collaboration with MLOps and platform teams
- Ensure fairness and mitigate model bias, applying techniques to identify and reduce demographic or systemic 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, to support NLP and conversational use cases
- Link model outputs to Success KPIs by quantifying value creation (e.g., time saved, accuracy improvements, or revenue impact)
Requirements
- 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 in cross functional environments
- Working knowledge of MLOps practices (e.g., containerization, CI/CD, model serving) to ensure models are production ready and scalable
- Hands on experience with Azure ML and GCP Vertex AI for model training, deployment, and monitoring
Full Job Posting
What You'll Be Doing
- We are looking for a Senior Data Scientist to design, build, and continuously improve machine learning models that enable predictive and intelligent decision making across business functions, ensuring accuracy, fairness, and robustness through rigorous experimentation, tuning, and monitoring.
Key Accountabilities
- Design and develop machine learning models for predictive, classification, or optimization use cases aligned to business needs.
- Own model training, tuning, and evaluation, using appropriate techniques to optimize performance, precision, and recall.
- Engineer and select relevant features, leveraging statistical and domain knowledge to improve model outcomes.
- Implement experiment tracking frameworks to ensure reproducibility and model comparability across versions.
- Establish and maintain model performance metrics, including accuracy, F1 score, AUC, etc., depending on use case.
- 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, in collaboration with MLOps and platform teams.
- Ensure fairness and mitigate model bias, applying techniques to identify and reduce demographic or systemic 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, to support NLP and conversational use cases.
- Link model outputs to Success KPIs by quantifying value creation (e.g., time saved, accuracy improvements, or revenue impact).
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 in cross functional environments.
- Working knowledge of MLOps practices (e.g., containerization, CI/CD, model serving) to ensure models are production ready and scalable.
- Hands on experience with Azure ML and GCP Vertex AI for model training, deployment, and monitoring.
What We Can Offer You
- Health care, child education contribution, remote and flexible working policies, exclusive employee discounts.
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