Data Scientist-Senior I

Primary Work Location - Chennai Job Summary: Takes ownership over select data science initiatives. Advances ACCs broad capabilities to use and deploy cutting edge data science and machine learning tools and methods in ACCs projects, platforms and products. Anchors current best practices by driving the design and build of reusable data science assets. Simultaneously, work to keep ACCs on the bleeding edge by understanding the very latest and most sophisticated methods and tools for grappling with extremely large scale and complex problems. Leads junior data scientists in modeling and development to support operations initiatives and strategic programs, through the use of descriptive, diagnostic, predictive, prescriptive and ensemble modeling, advanced statistical techniques, and use of database tools and/or other approaches in mathematical analysis. Applies understanding of ML Ops, CI/CD processes and machine learning / data engineering practices to ensure sustainable model development and provide recommendations on complex problems. Provides guidance to less senior team members to drive results. Works with cross-functional teams. Job Description: The Data Scientist Senior I plays a pivotal role, focused on driving data science innovation within ACCs, helping to define and build the ACCs organization and leading 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 I will contribute to the 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 datasets and solve difficult analytical problems, applying advanced methods. S/he will drive 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 Description / Responsibilities Understanding in depth both the business and technical problems ACCs aims to solveExploring data and crafting models to answer core business problems that may not have a common blueprintDriving the invention of new approaches and algorithms for tackling data intensive problemsPioneering R&D efforts to rapidly understand and assimilate state of the art methodsScaling up from laptop-scale to cluster scale problems by driving efforts to standardize and industrialize solutionsDelivering tangible value very rapidly, collaborating with diverse teams of varying disciplines and organizational backgroundsInteracting with senior technologists from the broader enterprise and outside of FedEx (partner ecosystems and customers) to create synergies and identify opportunities for improvementChampioning best practices for future reuse in the form of accessible, reusable patterns, templates, and code basesSkills / Abilities Technical background in computer science, data science, machine learning, artificial intelligence, statistics or other quantitative and computational scienceA track record of designing and deploying large scale technical solutions, which deliver tangible, ongoing valueDirect experience having built and deployed robust, complex production systems that implement modern, data scientific methods at scaleAbility 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 definedDemonstrated ability to deliver technical projects with a team, often working under tight time constraints to deliver valueAn engineering mindset, willing to make rapid, pragmatic decisions to improve performance, accelerate progress or magnify impactComfort with working with distributed teams on code-based deliverables, using version control systems and code reviewsSolid theoretical grounding in the mathematical core of the major ideas in data scienceStrong understanding of a class of modelling or analytical techniques, often supported by Masters- or Doctoral-level research in the subjectFluency in the mathematical primitives and generalizations of data science e.g., expertise in Linear Algebra, and Vector CalculusUse of agile and devops practices for project and software management including continuous integration and continuous deliveryDemonstrated expertise in working with some of the following common languages and tools: SKLearn, XGBoost, Tensorflow, Pytorch, MLlib and other core machine learning frameworks Python and other modern programming languages MLFlow, Databricks, AWS, Azure and other data tools and Prim