Sr. Data Scientist, Paid Media

Job Title: Senior Manager, Commercial Analytics & Data Science
Location: Remote (U.S.) – EST hours preferred
About The Company
We are a large-scale subscription media organization that relies heavily on advanced analytics and experimentation to guide marketing investment and growth strategy. The Commercial Analytics & Data Science team partners directly with marketing, finance, and product stakeholders to determine how and where marketing dollars should be deployed. The team’s work directly influences media investment decisions, customer acquisition strategy, and long-term subscriber growth.
Job Summary
The Senior Manager, Commercial Analytics & Data Science is an individual contributor role responsible for applying statistical modeling, experimentation, and machine learning to optimize marketing performance. This position focuses on measuring media effectiveness, designing incrementality tests, and developing marketing mix models that guide marketing investment decisions across the customer lifecycle.
You will work hands-on with Python, SQL, and cloud data environments to build models, analyze results, and communicate business implications to stakeholders. The ideal candidate combines strong data science skills with business acumen and a deep understanding of paid media performance and conversion drivers.
Key Responsibilities
Build and maintain marketing mix models to estimate incremental ROI and optimize media spend.
Design and evaluate paid media incrementality testing programs, including geo and matched-market experiments.
Develop machine learning models to predict marketing and subscription outcomes across acquisition and retention journeys.
Design and analyze controlled experiments, including sample sizing, measurement strategy, and evaluation.
Partner with marketing stakeholders to translate business questions into statistical analyses and actionable recommendations.
Deliver presentations explaining results, implications, and recommendations to technical and non-technical stakeholders.
Collaborate with engineering teams to productionize and monitor model performance.
Write complex SQL queries to prepare datasets for feature engineering and test analysis.
Build Python-based tools and libraries to automate analysis and testing workflows.
Support cross-functional teams by providing insights that influence media investment and marketing strategy.
Requirements
Education:
Master’s degree in Data Science, Statistics, Mathematics, Engineering, Computer Science, or related quantitative field preferred (relevant experience may substitute).
Experience
5+ years of hands-on data science experience.
Experience working with paid media performance data, ideally within a media agency or subscription-based business.
Experience designing and measuring incrementality testing and experimentation programs.
Experience building and deploying traditional machine learning models (not generative AI focused).
Experience working in cloud-based data environments.
Skills
Strong Python proficiency (team primarily codes in Python; SAS experience alone will not be sufficient).
Strong SQL for data extraction and validation.
Experience with marketing mix modeling frameworks (e.g., Robyn, Meridian, or similar).
Understanding of causal inference, A/B testing, and statistical modeling techniques.
Ability to communicate complex findings clearly to business stakeholders.
Collaborative mindset and ability to work independently in a cross-functional environment.
Why Join Us
This role sits at the center of how marketing investment decisions are made. Your models and analyses will directly influence millions of dollars in media spend and subscriber growth strategy. If you enjoy hands-on data science, real business impact, and solving messy real-world marketing problems, this is a highly visible and rewarding opportunity.

Work arrangement
No