Data Scientist

Location: Bengaluru

The Pricing Data Science team within Revenue Management at Fed Ex 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, Big Query, 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 & Dev Ops (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, Big Query)
Experience in pricing, revenue management, supply chain, or logistics analytics

Experience with model monitoring, drift detection, and production support