Job Description:
Interviews: Live, Technical Hands On
About the Role
We're looking for a Data Scientist to join a team focused on advertising inventory modeling and capacity forecasting for client's subscriber base. This team builds models that match commercials and ads to subscriber profiles based on viewing behavior, identifies gaps in that targeting, and works to fill them through marketing data. A core part of the role is forecasting available advertising capacity — balancing sold vs. unsold airtime, and understanding the difference between acquired inventory and Spectrum's own inventory, as well as true demand vs. true capacity.
The team works with a blend of client's proprietary subscriber data and third-party data sources. Notably, data from Cox — previously treated as third-party — became first-party data as of a recent company merger, and this shift is actively being incorporated into forecasting models.
This is a great opportunity for a data scientist who enjoys applied modeling work with real business impact, in a fully cloud-based environment.
What You'll Do
Build and maintain models that align advertising inventory with subscriber viewing profiles
Forecast advertising capacity, including sold vs. unsold airtime and acquired vs. owned (Spectrum) inventory
Work with both first-party (Charter, and now Cox) and third-party data sources
Incorporate newly integrated first-party data (from the Cox merger) into existing forecasting models
Collaborate with a cross-functional team, following established CI/CD practices
Take on light data engineering tasks as needed to support modeling work
What We're Looking For
Solid, practical data science experience — this is not a role requiring deep specialization or "absolute expert" level skills
Someone who understands what they're doing and can work independently on modeling problems
Experience working as part of a team, ideally with exposure to CI/CD processes
Comfort doing some data engineering work in support of modeling (not a pure modeling-only candidate)
Candidates from a Finance background are not a strong fit for this role
Tech Stack
Languages/Tools: Python, SQL
Time series & Forecasting Experience
Data Warehouse: Snowflake (corporate data warehouse)
Cloud: AWS (fully cloud-based — no on-prem infrastructure)
ML Tooling: Various data science libraries
Nice to Have: SageMaker, Airflow, PySpark