Data Scientist, Revenue Science (remote)

Key Skills

Java Programming, Python Programming, SQL, Azure, AWS, Machine learning techniques, Google Cloud Platform (GCP), Google Analytics, Statistical modeling, Large scale data processing, Flax Programming Language

Job Description

The Enterprise Data Science team, a part of Gannett’s Enterprise Data Organization, designs and develops decision science applications using machine learning models that enable our crossfunctional partners across Product, Content, Sales and Marketing teams to predict performance, better understand audiences and then engage them with meaningful and delightful content experiences.

What you’ll do:

This opportunity is for the Revenue data science hub. This team builds forecasting models to support Gannett’s Print, Digital and Advertising campaigns, and financial planning. In addition, the revenue science team builds AI/ML solutions that help optimize investments and thereby, maximize revenue across our various businesses. Our ideal candidate is a collaborative and innovative problem solver, who is profoundly handson and is passionate about applying stateoftheart and innovative solution frameworks to solve business problems and identify process improvement opportunities.

How you’ll make an impact:

• As a Sr. Data Scientist – Revenue, you will
• Have deep technical expertise in forecasting and optimization techniques, as well as experience in building, implementing, and driving consumption for related decision science applications.
• Develop and deliver machine learning and time series models, scenarioplanning engines and optimization solutions that drive measurable business impact.
• Have experience in scaling and maintaining machine learning models with knowhow of automated training, validation, and monitoring of these models.
• Any experience in building advertising auction prediction and bid optimization solutions is a plus.
• Ability to understand the Gannett business model deeply and to effectively translate model outcomes into actions and recommendations that lead to decisions and measurable ROI.
• Have deep experience in and ability to present compelling data stories to both technical and nontechnical stakeholders and Clevel executives.

What you’ll bring:

• An MS or PhD in a quantitative field. 5+ years of experience manipulating large data sets and implementing large scale endtoend forecasting and/or revenue optimization solutions.
• Handson coding proficiency and experience in Python (preferred) or any other statistical programming language.
• Experience working with raw data sources across multiple technologies and data sources.
• Expertlevel knowledge of and experience in using AI/ML techniques like time series, decision trees and other learning models to build and productionalize optimization applications.
• Experience working with cloud computing data environments like GCP (preferred), AWS, Azure etc.
• Experience working with Google Analytics and behavioral data is a plus but not required.