Data Scientist II
Primary Work Location - Chennai
Job Summary
Under general supervision, 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 supporting 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. Applies descriptive, diagnostic, predictive, prescriptive and ensemble modeling, statistical techniques, and use of database tools and/or other approaches in quantitative analysis of complex business situations. Provides recommendations to moderately complex issues through knowledge of data scientific methods and machine learning / data engineering practices. Works in tandem with data scientists in peer positions.
The Data Scientist II plays a pivotal role, focused on delivering data science innovation within ACCs, helping to define and build the ACCs organization, and executing 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 II 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 data sets and solve difficult analytical problems, applying advanced methods. S/he will execute 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 development and medium-scale deployments.
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
- Contributing to 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 contributing to efforts to standardize and industrialize solutions
- Delivering tangible value very rapidly, collaborating with diverse teams of varying backgrounds and disciplines
- Interacting with peer technologists from the broader enterprise and outside of FedEx (partner ecosystems and customers) to create synergies and identify opportunities for improvement
- Following 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
- Experience with 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
- Good understanding of a class of modelling or analytical techniques, often supported by Masters- or Doctoral-level research in the subject
- Experience with the mathematical primitives and generalizations of data science eg, 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:
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 frameworks
CPLEX, Gurobi and other similar optimization modeling packages
