The Internet Software & Services WPC Analytics Team (Wallet, Payments & Commerce is looking for an outstanding data scientist/Econometrician to play an integral role in helping the business make smarter decisions. You will partner with key players across Apple Pay, Payments, Commerce and Store Credit teams to perform econometric mix modeling to uncover the impact of key business and marketing changes on the business, especially with our newly launched Apple Card business.
This will include using large and complex data sources with advanced analytical techniques to deliver dynamic and intuitive forecasting tools that help the team understand what drives our business. In this role you will also perform complex scenario simulation modeling to help the business forecast the potential changes in the business and customer behavior that may occur with each new payments and commerce program.
The position reports to the head of Data Science and Applied Machine Learning for the IS&S Payments analytics team, and will work with another data scientist on our forecasting system and ad hoc requests. The ideal candidate is a self-starter with wide range of technical skills, and is highly proficient in turning data discoveries into analytical insights. This is a hands-on role involving a mix of exploratory data analysis and predictive model development targeted at linking business actions and metrics, market conditions, and consumer behaviors to key marketing & business outcomes.
Conceive and design end to end optimized data platforms and customer analytic scripted solutions using SAS, TeraData and Python. Experience building forecasting and or econometric systems in Spark and Hadoop with Python or R highly preferred. You have experienced application knowledge of Applied Regression techniques including, but not limited to Linear, Logistic, Mixed models, Distributed Lags, Time Series, General Linear models & Simultaneous equations. Ideal candidates will also have experience in the use of Neural Network models for sales forecasting and GAM models. You are also an authority in application of Multivariate techniques like Random Coefficient models, Canonical Discriminant models, Exploratory and Confirmatory Factor Analysis, Canonical Correlations. We seek an expert in Fixed vs random effect models specially in their use in uncovering the attribution effects of mass media investments. You have strong working knowledge of database structures and data warehousing principles and have solid experience in SQL with the ability to ETL both structured and unstructured data from various sources; experience with Teradata and Spark/Hadoop clusters highly preferred.
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