Data Science & AI

What are your contributions to the team?

•  Translate business challenges into measurable forecasting, optimization, NLP, retrieval, classification, risk, or recommendation problems.
•  Explore, profile, and validate governed enterprise data.
• Develop reproducible feature-engineering, training, validation, and scoring pipelines using Databricks and Azure.
• Establish appropriate analytical baselines and business performance measures.
• Build and validate models using cross-validation, back-testing, uncertainty analysis, and business-relevant metrics.
   
• Develop document-intelligence and RAG solutions with grounded outputs, citations, retrieval evaluation, guardrails, and human review.
• Develop forecasting and inventory-optimization solutions that account for intermittent demand, uncertainty, service levels, and operational constraints.
• Develop classification and risk models using suitable techniques for imbalanced data, calibration, explainability, and cost-sensitive decisions.
• Operationalize models using MLflow, model registries, automated testing, deployment controls, monitoring, drift detection, and retraining plans.   
• Apply privacy, security, lineage, explainability, bias, robustness, auditability, and incident-response controls throughout the AI lifecycle.
• Clearly communicate assumptions, limitations, trade-offs, and recommendations.    Partner with full stack and platform engineers to integrate models into production workflows and user experiences.
• Maintain model documentation, validation evidence, monitoring plans, and support procedures.
• Partner with cross-functional business, technology, data, governance, and delivery stakeholders across the enterprise.
• Translate business requirements into maintainable technical designs and acceptance criteria.

How to thrive in this role?

• You hold a bachelor’s degree in data science, computer science, statistics, mathematics, engineering, operations research, or a related quantitative field.
• You have 5+ years of experience developing applied data science, machine learning, forecasting, optimization, NLP, or risk-modeling solutions in business or enterprise environments.
• You have strong Python and SQL skills, with hands-on experience using pandas, NumPy, scikit-learn, Spark/PySpark, and Databricks.
• You can frame business problems into analytical targets, constraints, acceptance criteria, measurable outcomes, and production-ready recommendations.
•  You apply sound model-development and validation practices, including baseline development, cross-validation, back-testing, error analysis, explainability, and business KPI definition.
• You communicate clearly with technical and non-technical stakeholders, explaining assumptions, results, limitations, trade-offs, and recommended actions.
• You thrive in fast-paced, collaborative environments and are fluent in English and French, spoken and written.