Data Scientist

The Pricing Data Science team within Revenue Management at FedEx develops advanced analytics, machine learning models, and intelligent applications that power pricing strategy, revenue optimization, and decision support across the enterprise. Our work directly influences margin performance, customer segmentation, contract pricing, and strategic initiatives.We operate at the intersection of data science, cloud engineering, and production-grade application development.Role Overview:We are seeking a highly skilled AI/ML Engineer who can build, deploy, and scale machine learning models and full-stack applications in modern cloud environments (Azure and/or GCP).This role requires strong end-to-end ownership — from model development to production deployment and application integration — with an emphasis on scalable, secure, and enterprise-ready systems.Key Responsibilities:AI / Machine LearningDesign, build, and optimize machine learning models for pricing, forecasting, and revenue optimization use casesDevelop production-grade ML pipelines for training, evaluation, and inferenceImplement MLOps best practices including versioning, monitoring, retraining, and governanceCollaborate with data scientists and business stakeholders to translate business problems into scalable AI solutionsCloud & InfrastructureArchitect and deploy ML solutions in Azure and/or GCPBuild scalable cloud-native architectures leveraging services such as:Azure ML, Databricks, Synapse, AKSGCP Vertex AI, BigQuery, GKEImplement CI/CD pipelines for model and application deploymentEnsure system reliability, security, performance, and cost optimizationApplication DevelopmentDevelop and scale web-based AI applications using:React.js (preferred) or other modern front-end frameworks (Angular, Vue, etc.)Backend frameworks such as Python (FastAPI, Flask), Node.js, or similarBuild APIs to expose ML models for internal business consumptionIntegrate front-end interfaces with ML services and backend systemsContainerization & DevOps (Strong Plus)Containerize applications and ML services using DockerDeploy and manage workloads in Kubernetes (AKS, GKE, or similar)Implement monitoring and observability tools for production systemsRequired Qualifications :Bachelor’s or master’s degree in computer science, Data Science, Engineering, or related field3-6 years of experience building ML models in production environmentsStrong proficiency in PythonHands-on experience with Azure and/or GCP cloud ecosystemsExperience building scalable web applications using React.js or comparable frameworksExperience building RESTful APIs and integrating ML services into applicationsSolid understanding of software engineering best practices (testing, version control, CI/CD)Preferred QualificationsExperience with Docker and Kubernetes in production environmentsFamiliarity with MLOps frameworks (MLflow, Kubeflow, Vertex AI pipelines, etc.)Experience with large-scale data processing (Spark, Databricks, BigQuery)Experience in pricing, revenue management, supply chain, or logistics analyticsExperience with model monitoring, drift detection, and production support

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