Data Scientist Principal

About the Role

The Data Scientist Principal leads advanced analytics initiatives across the organization, designs end-to-end machine learning solutions, and mentors data science teams. This individual partners closely with stakeholders in product, engineering, and business units to translate complex data into actionable insights that drive strategic decision-making and innovation. Takes end-to-end ownership and responsibility over critical data science initiatives. Advances DW 2.0’s broad capabilities to use and deploy cutting edge data science and machine learning tools and methods in projects, platforms and products. Anchors current best practices by championing the design and build of reusable data science assets. Combines knowledge of data scientific methods, CI/CD, statistics, and machine learning / data engineering practices to provide recommendations on the most organizationally critical and complex problems. Advises junior data scientists, managers, and those in less senior positions. You will contribute to our mission within a collaborative, mentorship-driven environment, working hands-on with the Azure and / or Google Cloud Platform (GCP) ecosystems.

Key Responsibilities

• Lead and own high-impact data science initiatives within the DW 2.0 data monetization scope and other initiatives as needed, from problem framing through deployment and monitoring.
• Apply advanced AI techniques (LLMs, reinforcement learning, graph ML, and optimization algorithms) to address complex, high-impact challenges across supply chain operations such as routing, inventory balancing, capacity planning, and risk mitigation.
• Architect and develop scalable machine learning models (e.g., predictive, prescriptive, NLP, computer vision) for production.
• Lead rapid prototyping and iterative experimentation: leverage modern AI stacks (Azure OpenAI, Vertex AI, LangChain, vector databases) to design lean experiments, measure impact, and iterate quickly while avoiding dependency bottlenecks.
• Handle pressure with poise, balancing urgent requests with long-term project goals and ensuring reliable outcomes.
• Build and operationalize end-to-end AI/ML systems, including LLM pipelines, prompt engineering strategies, fine-tuning, model evaluation, guardrails, and monitoring for performance, drift, and responsible AI compliance.
• Collaborate with engineering teams to integrate data pipelines, ensure model reliability, and optimize performance.
• Define metrics and success criteria; perform rigorous statistical analysis, A/B testing, and AI model evaluation (hallucination detection, accuracy, latency, relevance) to validate system effectiveness.
• Translate business objectives into analytical approaches, interpret results, and present clear storytelling and data-driven recommendations to senior leadership.
• Mentor and coach junior and mid-level data scientists; establish best practices in coding, model development, and documentation.
• Drive innovation by researching and prototyping emerging techniques, tools, and frameworks in machine learning, deep learning, and AI.
• Partner with legal, security, and data governance teams to ensure data usage adheres to privacy, security, and contractual obligations; develop compliant mechanisms to enable safe, fast experimentation.
• Communicate results and recommendations clearly to senior leadership and cross-functional stakeholders; translate complex analyses into actionable business insights.
• Cultivate deep domain expertise in FedEx data and tools, taking direction from senior team members and contributing to knowledge sharing.
• Collaborate with business partners and subject matter experts to translate complex questions into clear analytical insights and present findings effectively.

Knowledge, Skills, and Abilities

• Extensive knowledge in advanced data science and machine learning methods, including the iterative development of analysis pipelines to provide insights at scale.
• Strong experience as a leader of multi-functional project teams.
• Excellent interpersonal skills and the ability to present and communicate effectively to executive audiences.
• Proficiency in SQL, Python, and/or R.
• Foundational knowledge of machine learning libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
• Hands-on experience with Azure (e.g., Data Factory, Synapse, Databricks, Blob Storage) and / or Google Cloud Platform (GCP) and its core analytics/ML services (BigQuery, Vertex AI, Dataflow, Pub/Sub).
• Demonstrated ability to leverage various APIs for data manipulation and integration.
• Experience with at least one data visualization tool or package (e.g., Tableau, Power BI, Spotfire, Shiny, Plotly, Matplotlib, Seaborn).
• Solid understanding of ETL concepts, relational databases (e.g., Teradata, Oracle), and working with large-scale datasets.
• Proficiency with version control (git) and familiarity with MLOps/DevOps principles (CI/CD, model tracking, deployment workflows).
• Demonstrated superior analytical skills with diverse analytics, data types, and statistical software and applications.
• Outstanding Interpersonal skills, written, and oral communication skills.
• Proven Leadership skills.

Preferred Skills and Experience

• Proven background with enterprise AI solutions, agentic architectures, or scalable LLM platforms.
• Proficiency in Power BI for building interactive dashboards, reports, and data visualizations.
• Experience in your industry domain (e.g., finance, healthcare, e-commerce).
• Familiarity with MLOps practices and tools (MLflow, Kubeflow, Airflow).
• Prior experience leading cross-functional teams and managing stakeholder relationships.
• Publications or contributions to open-source projects in machine learning or data science.

Minimum Education

Master's degree or equivalent in a quantitative discipline required.

Minimum Experience

Proven expert and nine (9) years work experience in innovative measurement and analysis, quantitative business problem solving, solutions implementation, operations analysis, marketing analysis, simulation development and/or predictive analytics.

Key Skills/Competency

• Data Science
• Machine Learning
• Python
• SQL
• Azure
• Google Cloud Platform (GCP)
• Artificial Intelligence (AI)
• Statistical Modeling
• LLMs
• MLOps