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
Expertise in R, SQL, Python and/or any other high-level languages.
Exploratory data analysis (EDA), data engineering and development of advanced analytics models.
Experience in development of AI and ML using platforms like VertexAI, Databricks or Sagemaker, and familiarity with available frameworks like PyTorch, Tensorflow and Keras.
Experience applying models from small to medium scaled problems.
Strong analytical skills and attention to detail.
Able to engage key business and executive-level stakeholders to translate business problems to a high-level analytics solution approach.
Expertise with statistical techniques, machine learning, and/or operations research and their application in business.
Deep understanding of data management pipelines and experience in launching moderate scale advanced analytics projects in production.
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.
Core AI / Machine Learning knowledge and application in supervised and unsupervised learning domains.
Familiarity with Java or C++ is a plus.
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 audiences.
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.
