Data Scientist I and II (GCP & AI Engineer)

Federal Express Corporation is seeking early-career Data Scientists I and II to contribute to innovative solutions that deliver business value. The roles involve designing, building, and deploying analytics and machine learning solutions while collaborating with cross-functional teams to drive insights from big data.

Responsibilities:

• Develop and implement predictive and descriptive analytics on structured and unstructured data, encompassing classification, regression, clustering, and hypothesis testing
• Clean, curate, and transform raw data from existing tables and systems using SQL and Python to prepare high-quality datasets for model training and evaluation
• Write efficient SQL queries and Python scripts to extract, manipulate, and explore data housed within BigQuery and Cloud Storage (GCS)
• Design and create intuitive dashboards and visualizations in Looker and Looker Studio to communicate metrics and insights clearly to team members and stakeholders
• Actively participate in code reviews, enforce version control (Git), and apply foundational CI/CD and MLOps practices (such as model tracking) for seamless deployment
• Explore Generative AI concepts and foundational models using Vertex AI Generative AI Studio to identify potential business use cases
• Collaborate with business partners to translate operational questions into analytical insights, while cultivating deep domain expertise in FedEx data systems and business operations
• Lead the complete lifecycle of analytics and ML projects, from problem definition and data discovery to feature engineering, model development, validation, deployment, and continuous monitoring
• Architect, develop, and productionize advanced predictive and prescriptive models (classification, regression, clustering, time series, optimization), effectively serving them via Vertex AI Pipelines and Endpoints
• Design, develop, and deploy practical Generative AI solutions (e.g., Retrieval-Augmented Generation/RAG architectures, prompt design) using Vertex AI Gen AI capabilities
• Design and implement robust feature stores and transformation logic to feed training and real-time inference workflows, ensuring model input consistency and drift mitigation
• Expertly design, build, and integrate RESTful APIs (e.g., FastAPI, Flask) for seamless model serving and microservice-based ML system integration
• Implement advanced MLOps and CI/CD strategies for models and serving pipelines, encompassing automated testing, deployment pipelines, model registry/tracking, and comprehensive drift and performance monitoring
• Optimize data models, analytical views, and SQL queries to support efficient, low-latency machine learning training and inference workloads
• Develop compelling, interactive dashboards using Looker/Looker Studio to translate complex insights into clear business actions and quantifiable KPIs
• Mentor and technically guide junior data scientists and analysts, actively contributing to team-wide best practices, documentation, and troubleshooting production incidents
• Cultivate deep domain expertise in FedEx data and business operations to align ML efforts with immediate team goals

Requirements:

• Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, or a closely related quantitative field
• 0–3 years of relevant experience (internships, academic projects, or co-ops are highly valued)
• Strong communication skills, an innate eagerness to learn, and a proactive, collaborative problem-solving mindset
• Proficiency in SQL and Python
• Foundational knowledge of machine learning libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch)
• Hands-on exposure to Google Cloud Platform (GCP), specifically BigQuery, Vertex AI, and Cloud Storage (GCS) for data retrieval and modeling
• Demonstrated ability to interact with and leverage RESTful APIs for consuming model inputs and integration
• Experience with at least one visualization tool (e.g., Looker, Looker Studio, Tableau) or Python plotting libraries (Matplotlib, Seaborn)
• Foundational understanding of Git, relational database concepts, and basic MLOps/DevOps workflows
• Expert proficiency in Python and SQL
• Extensive experience with leading ML frameworks (scikit-learn, XGBoost, TensorFlow, or PyTorch) and a proven track record of deploying them to production environments
• Hands-on experience developing solutions utilizing GenAI models (e.g., working with LLMs, prompt engineering, and utilizing Vertex AI Model Garden)
• Deep hands-on experience with Google Cloud Platform (GCP) for end-to-end ML, specifically BigQuery, Vertex AI, Vertex AI Feature Store, and Cloud Storage (GCS)
• Proficiency in designing, building, and securing APIs (e.g., FastAPI, Flask) to expose ML microservices
• Expertise in performance tuning, SQL query optimization, and the design of efficient, scalable models for ML data preparation
• Master's degree (or equivalent) in Computer Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field
• At least two (2) years of professional experience applying data science (e.g., machine learning, artificial intelligence, statistical analysis), operations research (e.g., optimization, algorithms, mathematical modeling), and data analytics to reduce costs, enhance profitability, and improve customer experience
• Familiarity with GCP cloud environments (e.g., passing the Google Cloud Digital Leader or Associate Cloud Engineer exam is a plus)
• Academic or personal project experience with Large Language Models (LLMs) or prompt engineering
• Internship or project experience in transportation, logistics, or supply chain
• Professional Google Cloud Machine Learning Engineer certification
• Experience with monitoring, observability, and automated data-quality tools (e.g., Great Expectations, GCP Cloud Monitoring, Cloud Logging for ML model performance)
• Domain expertise in logistics, transportation, supply chain, or related industries