Data Scientist-Senior

• 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
• Driving 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 driving efforts to standardize and industrialize solutions
• Delivering tangible value very rapidly, collaborating with diverse teams of varying disciplines and organizational backgrounds
• Interacting with senior technologists from the broader enterprise and outside of FedEx (partner ecosystems and customers) to create synergies and identify opportunities for improvement
• Championing best practices for future reuse in the form of accessible, reusable patterns, templates, and code bases

Skills / Abilities

• Technical background in computer science, data science, machine learning, artificial intelligence, statistics or other quantitative and computational science
• A track record of designing and deploying large scale technical solutions, which deliver tangible, ongoing value
• Direct experience having built and deployed robust, complex production systems 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, and to work through problems as they are still being defined
• 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
• Comfort with working with distributed teams on code-based deliverables, using version control systems and code reviews
• Solid theoretical grounding in the mathematical core of the major ideas in data science
• Strong understanding of a class of modelling or analytical techniques, often supported by Masters- or Doctoral-level research in the subject
• 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 continuous integration and continuous delivery
• Demonstrated expertise in working with some of the following common languages and tools: