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Data Scientist
Minor Hotels Europe and AmericasSenior Data Scientist leading cross-functional AI/ML initiatives at Capgemini Engineering. Focusing on model development, optimization, and collaboration with data analytics teams.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in end-to-end model development for pricing and demand forecasting, utilizing Azure ML and Databricks. Proficient in implementing prescriptive analytics and model interpretability tools to drive actionable business insights.
Highest-signal resume keywords
Data Science ExperiencePython ProficiencyAzure ML ExpertiseExperimental Design KnowledgeModel Interpretability Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Model DevelopmentDemand ForecastingElasticity EstimationLinear ProgrammingMixed Integer ProgrammingReinforcement LearningFeature Store MaintenanceA/B TestingStatistical TestingCausal Inference
Soft Skills
Stakeholder AlignmentCross-Functional CommunicationInsight Communication
Tools & Technologies
Azure MLDatabricksMLflowPythonScikit-learnXGBoostSpark/DeltaPyTorchTensorFlow
Industry Keywords
Prescriptive AnalyticsModel MonitoringDrift ChecksRevenue ManagementPricing Uplift
Tech Stack
Tools & technologiesAzurePythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
Key responsibilities & impact- Owning end-to-end model development for pricing, demand forecasting, and elasticity estimation; productionizing models in Azure ML and Databricks
- Implementing prescriptive analytics through optimization with Linear Programming, Mixed Integer Programming or Reinforcement Learning
- Implementing and maintaining feature stores, model monitoring workflows, and drift checks using MLflow (metrics, alerts, lineage)
- Designing and executing A/B tests or quasi-experiments to measure revenue, pricing uplift, and PCP attach rate impact
- Applying SHAP/LIME and other model interpretability tools to explain drivers of model behavior to Revenue Management partners
Requirements
What you’ll need- 2–4 years of hands-on Data Science experience delivering production-grade ML solutions
- Proficiency in Python (scikit-learn, XGBoost), Spark/Delta, SQL, Azure ML, Databricks, and MLflow; familiarity with PyTorch or TensorFlow is a plus
- Strong understanding of experimental design, statistical testing, and causal inference basics
- Ability to translate technical concepts into actionable business insights; skilled in stakeholder alignment and cross-functional communication
- Experience communicating insights, assumptions, risks, and trade-offs in clear, concise, and executive-ready narratives
Benefits
Comp & perks- Health insurance
- Flexible work arrangements
- Professional development