We are looking for a candidate to join our Commercial Analytics and Data Science team. We are seeking a person that applies strong programming and statistical skills to experimentation and machine learning model development.
This position plays a key role in optimizing media spend through building marketing mix models and designing and measuring incrementality tests. You will also support test design and evaluation of marketing initiatives across multiple channels spanning the customer lifecycle. A keen interest and ability to advance organizational capabilities through research and innovation initiatives across areas such as AI, causal inference, and experimentation is critical.
Responsibilities
What you’ll do:
Build marketing mix models to estimate incremental ROI of marketing channels and optimize spend. Experience using tools such as Robyn and Meridian is preferred.
Design and measure paid media incrementality tests using geo-experiments
Support testing marketing initiatives to drive app engagement and increase subscriber retention across the customer lifecycle applying proper statistical rigor. Responsibilities include experimental design (sample sizing, timing), quality checking implementation, and test measurement and evaluation.
Build machine learning models for predicting key outcomes used to inform marketing tactics across digital acquisition and user journey orchestration
Build web applications using Python providing daily monitoring of test results and model results and insights
Deliver presentations describing experimental results and recommendations to business partners
Work closely with engineering to productionize and monitor model performance
Contribute to research and innovation initiatives to further organizational capabilities across AI, causal inference, and statistical methods
Represent Commercial Analytics and Data Science capabilities and deliver data-driven insights within cross-functional teams to help achieve business objectives
Develop complex SQL queries combining data from a wide variety of sources in preparation of feature engineering and test analysis
Create custom Python libraries available for team use to automate key methods
Requirements
What you’ll need:
Bachelor’s or Master's degree in data science, computer science, statistics, engineering, or related quantitative field.
5 or more years of experience as a Data Scientist
Background in paid media, ideally with experience at large media companies
Experience building Marketing Mix Modeling (MMM) models
Experience with paid media incrementality measurement/testing
Strong foundation in SQL
Understanding or experience working within an enterprise data warehouse environment
Experience working in or partnering with media agencies
Background in subscription or e-commerce businesses
Experience building web apps using frameworks such as Streamlit, Shiny, or Dash is preferred
Capability to build, tune and explain machine learning models for supervised and unsupervised learning. Methods include, but are not limited to, regression, clustering, and ensemble methods.
Understanding of statistical concepts, lift analysis, and A/B testing fundamentals
Ability to produce data visualizations for explaining technical concepts