Sr. Data Scientist, tvScientific
About the role
About tvScientific
tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.
As a key member of our Data Science team, you'll be responsible for turning data into actionable insights, driving business decisions, and owning analytics projects from inception to completion. Day to day, your role will involve developing customized reporting and analytics tools to meet the unique needs of our clients.
What you'll do
- Write production code in Python.
- Design, launch, and analyze experiments to optimize ad campaigns.
- Create reporting and analytics tools for the Data Science Team's customers.
- Translate insights from reporting tools into tvScientific's Data Science Product.
What we're looking for
- Ability to write and review production-level code in Python.
- Strong statistics and ML fundamentals.
- Desire to work in a fast-growing environment—comfortable with ambiguity, ownership, scaling new products, and an experimental, iterative development process.
- Experience defining and measuring the utility of new reporting and analytics tools.
- Ability to quickly translate between DS Analytics & DS Product team.
- Strong track record of providing high-quality service to the DS team’s customers/stakeholders.
- Adtech or CTV experience.
- Teaching experience is a plus.
- Big data experience with Scala, Apache Spark, Apache Beam, and AWS Athena is a plus.
- Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
- Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
- High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables
In-Office Requirement Statement
- We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.