Data Scientist

Description:

Takes ownership and responsibility over major data science initiatives. Advances Dataworks’ broad capabilities to use and deploy cutting edge data science and machine learning tools and methods in Dataworks projects, platforms and products. Simultaneously works to keep Dataworks on the leading edge by understanding and implementing the very latest and most sophisticated methods and tools for grappling with extremely large scale and complex problems. Leads modeling and development to support operations initiatives, strategic programs and new products/solutions, through the use of advanced descriptive, diagnostic, predictive, prescriptive and ensemble modeling, advanced statistical techniques, and complex mathematical modeling/tool development. Develops solutions supporting the movement of data and information assets following API-First / Service-Oriented Architecture principles. Leverages proficiency in ML Ops, CI/CD processes and machine learning / data engineering practices to ensure sustainable model development and provide recommendations on highly complex problems. Mentors less senior team members to drive results. Leads and works with cross-functional teams.

Job Responsibilities

The Data Scientist plays a pivotal role, focused on creating and driving data science innovation within Dataworks, helping define and build the Dataworks 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 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.

Key Areas Of Focus

 Understanding in depth both the business and technical problems Dataworks 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/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, Scala, R, C++, Java and other modern programming languages
 MLFlow, Databricks, Spark, Kafka, Hive, Hadoop and other data tools and frameworks
 CPLEX, Gurobi and other similar optimization modeling packages