Job Description: The Data Scientist Senior II plays a pivotal role, focused on creating and driving data science innovation within ACCs, helping define and build the ACCs organization, and enabling the delivery of key business initiatives. S/he acts as a “universal translator” between IT, business, software engineers and data engineers, collaborating with these multi-disciplinary teams. The Data Scientist Senior II will contribute to the creation and adherence of technical standards for data science and machine learning, including the design and construction of reusable data assets. S/he will work with large data sets and solve difficult analytical problems, applying advanced methods. S/he will lead the creation and implementation of solutions from concept to production, using current and emerging technologies to evaluate trends and develop actionable insights and recommendations. Day-to-day, s/he will be deeply involved in code reviews and large-scale deployments. S/he will also provide mentorship and guidance to junior data scientists to support the continued training and up-skilling of the Data Science team. Job Responsibilities Understanding in depth both the business and technical problems ACCs aims to solve Exploring data and crafting models to answer core business problems that may not have a common blueprint Leading the invention of new approaches and algorithms for tackling data intensive problems Pioneering R&D efforts to rapidly understand and assimilate state of the art methods Scaling up from “laptop-scale” to “cluster scale” problems by leading efforts to standardize and industrialize solutions Delivering tangible value very rapidly, collaborating with diverse teams of varying disciplines Interacting with senior technologists from the broader enterprise and outside of FedEx (partner ecosystems and customers) to create synergies and identify opportunities for improvement Codifying best practices for future reuse in the form of accessible, reusable patterns, templates, and code bases Skills and Abilities Technical background in computer science, data science, machine learning, artificial intelligence, statistics or other quantitative and computational science A compelling track record of Data Science / Engineer expertise, designing and deploying large scale technical solutions, which deliver tangible, ongoing value- Direct experience having built and deployed robust, complex production systems through Cloud technologies that implement modern, data scientific methods at scale Ability to context-switch, to provide support to dispersed teams which may need an “expert hacker” to unblock an especially challenging technical obstacle Demonstrated ability to deliver technical projects with a team, often working under tight time constraints to deliver value An ‘engineering’ mindset, willing to make rapid, pragmatic decisions to improve performance, accelerate progress or magnify impact Ability to work with distributed teams on code-based deliverables using version control Solid theoretical grounding in the mathematical core of the major ideas in data science Expert level understanding of a class of modelling or analytical techniques, often supported by Masters- or Doctoral-level research in the subject Deep fluency in the mathematical ‘primitives’ and generalizations of data science – e.g., expertise in Linear Algebra, and Vector Calculus Use of agile and devops practices for project and software management including CI/CD; process improvement and quality management experience (e.g., Lean, Six Sigma, QDM expert) Demonstrated expertise in working with some of the following common languages and tools: SKLearn, XGBoost, Tensorflow, Pytorch, MLlib and other core ML frameworks Python and other modern programming languages MLFlow, Databricks, AWS, Azure and other data tools and frameworks CPLEX, Gurobi and other similar optimization modeling packages Minimum Qualifications Master’s Degree or equivalent in computer science, operations research, statistics, applied mathematics or related quantitative discipline. Directly related PhD preferred. Five to Seven (5-7) years’ work experience in applying data science (machine learning, artificial intelligence, statistical analysis), operations research (optimization, algorithms, mathematical modeling), and data analytics modeling to decrease cost, increase profitability, and improve customer experience. Extensive knowledge in advanced data science, statistical analysis, and machine learning methods, including the iterative development of analysis pipelines to provide insights at scale. Extensive experience conducting end-to-end analyses, including data gathering and requirements specification, processing, analysis and presentation. Strong familiarity with the transportation industry, competitors, and evolving technologies. Experience providing leadership in a general planning or consulting setting. Experience as a leader or a senior member of multi-functional project teams. Strong human relations, organizational / time management, project management, and software development skills. Excellent interpersonal skills and the ability to present and communicate effectively to executive audiences.

About FedEx

FedEx connects people and possibilities through our worldwide portfolio of shipping, transportation, e-commerce and digital supply chain services. For decades, we’ve been innovating to deliver more for you. Strengthening supply chains with our global network. Simplifying logistics. Enhancing tracking and visibility. And using data from every journey to make your experience better. Our people are the foundation of our success, and FedEx has consistently ranked among the world’s most admired and trusted employers. We inspire our global workforce of more than 575,000 team members to remain absolutely, positively focused on safety, the highest ethical and professional standards, and the needs of their customers and communities.