RESPONSIBILITIES
• Defines key data sources from UPS and external sources to deliver models.
• Develops and implements pipelines that facilitates data cleansing, data transformations, data enrichments from multiple sources (internal and external)  that  serve as inputs for data and analytics systems. 
• For larger teams, works with data engineering teams to validate and test data and model pipelines identified during proof of concepts 
• Develops data design based on the exploratory analysis of large amounts of data to discover trends and patterns that meet stated business needs.
• Defines model key performance indicator (KPI) expectations and validation, testing, and re-training of existing models to meet business objectives.
• Reviews and creates repeatable solutions through written project documentation, process flowcharts, logs, and commented clean code to produce datasets that can be used in analytics and/or predictive modeling.
• Synthesizes insights and documents findings through clear and concise presentations and reports to stakeholders.
• Presents operationalized analytic findings and provides recommendations.
• Incorporates best practices on the use of statistical modeling, machine learning algorithms, distributed computing, cloud-based AI technologies, and run time performance tuning with the goal of deployment and market introduction
• Leverages emerging tools and technologies together with the use of open-source or vendor products in the creation and delivery of insights that support predictive and prescriptive solutions.

QUALIFICATIONS
Requirements: 

•Ability to take a data science problem from start to finish, use pytorch/tensorflow to build the full model product.

• Strong analytical skills and attention to detail. 
• Able to engage key business and executive-level stakeholders to translate business problems to high level analytics solution approach.
• Expertise with statistical techniques, machine learning or operations research and their application in business applications.
• Expertise in R, SQL, Python.
• Deep understanding of data management pipelines and experience in launching moderate scale advanced analytics projects in production at scale.
• Demonstrated experience in Cloud-AI technologies and knowledge of environments both in Linux/Unix and Windows. 
• Experience implementing open-source technologies and cloud services; with or without the use of enterprise data science platforms.
• Solid oral and written communication skills, especially around analytical concepts and methods. 
• Ability to communicate data through a story framework to convey data-driven results to technical and non-technical audience.
• Master’s Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.