Ignite your career as an early-career Data Scientist, playing a pivotal role in designing, building, and deploying innovative solutions that deliver tangible business value. You will contribute to our mission within a collaborative, mentorship-driven environment, working hands-on with the Google Cloud Platform (GCP) ecosystem under the guidance of senior team members.
Works as part of a team to employ scientific methods and data-discovery tools to find new patterns, insights, and relationships in big data; extract meaningful information from multiple large data sets and develop solutions that quickly and visually communicate results. Participates and collaborates in discussions across business and IT teams to understand data product needs. Analyzes data from various databases to drive optimization and improvement of product development and business strategies. Independently works on analyzing and interpreting data to identify trends, insights, and logical data driven solutions for business problems. Develops analytical models and algorithms which apply data to identify business improvement insights and inform the development of processes and tools (e.g. dashboards, other visualizations) for monitoring, analyzing, and evaluating analytical model performance. Ensures accuracy and quality of data, including reconciliation from disparate sources. Partners with other team members to collaborate with stakeholders, customers, and other functional teams. Works alongside other data scientists to understand business questions, identify opportunities to leverage data to drive business solutions, implement models, and monitor outcomes.
Responsibilities
Foundational Modeling:
Develop and implement predictive and descriptive analytics on structured and unstructured data, encompassing classification, regression, clustering, and hypothesis testing.
Data Preparation & Feature Engineering:
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.
Data Retrieval & Manipulation:
Write efficient SQL queries and Python scripts to extract, manipulate, and explore data housed within BigQuery and Cloud Storage (GCS).
Business Intelligence:
Design and create intuitive dashboards and visualizations in Looker and Looker Studio to communicate metrics and insights clearly to team members and stakeholders.
MLOps & Version Control:
Actively participate in code reviews, enforce version control (Git), and apply foundational CI/CD and MLOps practices (such as model tracking) for seamless deployment.
GenAI Exploration:
Explore Generative AI concepts and foundational models using Vertex AI Generative AI Studio to identify potential business use cases.
Collaboration & Domain Growth:
Collaborate with business partners to translate operational questions into analytical insights, while cultivating deep domain expertise in FedEx data systems and business operations.
Minimum Qualifications
Education:
Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, or a closely related quantitative field.
Experience:
0–3 years of relevant experience (internships, academic projects, or co-ops are highly valued).
Communication:
Strong communication skills, an innate eagerness to learn, and a proactive, collaborative problem-solving mindset.
Required Technical Skills
Core Languages:
Proficiency in SQL and Python.
ML Frameworks:
Foundational knowledge of machine learning libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
GCP Analytics Services:
Hands-on exposure to Google Cloud Platform (GCP), specifically BigQuery, Vertex AI, and Cloud Storage (GCS) for data retrieval and modeling.
API Integration:
Demonstrated ability to interact with and leverage RESTful APIs for consuming model inputs and integration.
Data Visualization:
Experience with at least one visualization tool (e.g., Looker, Looker Studio, Tableau) or Python plotting libraries (Matplotlib, Seaborn).
Version Control & DevOps:
Foundational understanding of Git, relational database concepts, and basic MLOps/DevOps workflows.
Preferred Qualifications
GCP Fundamentals:
Familiarity with GCP cloud environments (e.g., passing the Google Cloud Digital Leader or Associate Cloud Engineer exam is a plus).
Advanced GenAI:
Academic or personal project experience with Large Language Models (LLMs) or prompt engineering.
Industry Context:
Internship or project experience in transportation, logistics, or supply chain.