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 Learning

Design, build, and optimize machine learning models for pricing, forecasting, and revenue optimization use cases

Develop production-grade ML pipelines for training, evaluation, and inference

Implement MLOps best practices including versioning, monitoring, retraining, and governance

Collaborate with data scientists and business stakeholders to translate business problems into scalable AI solutions

Cloud & Infrastructure

Architect and deploy ML solutions in Azure and/or GCP

Build scalable cloud-native architectures leveraging services such as:

Azure ML, Databricks, Synapse, AKS

GCP Vertex AI, BigQuery, GKE

Implement CI/CD pipelines for model and application deployment

Ensure system reliability, security, performance, and cost optimization

Application Development

Develop 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 similar

Build APIs to expose ML models for internal business consumption

Integrate front-end interfaces with ML services and backend systems

Containerization & DevOps (Strong Plus)

Containerize applications and ML services using Docker

Deploy and manage workloads in Kubernetes (AKS, GKE, or similar)

Implement monitoring and observability tools for production systems

Required Qualifications :

Bachelor's or master's degree in computer science, Data Science, Engineering, or related field

3-6 years of experience building ML models in production environments

Strong proficiency in Python

Hands-on experience with Azure and/or GCP cloud ecosystems

Experience building scalable web applications using React.js or comparable frameworks

Experience building RESTful APIs and integrating ML services into applications

Solid understanding of software engineering best practices (testing, version control, CI/CD)

Preferred Qualifications

Experience with Docker and Kubernetes in production environments

Familiarity 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 analytics

Experience with model monitoring, drift detection, and production support