Data Scientist, Broadcast

Data Scientist, Broadcast Analytics

The Data Scientist, Broadcast Analytics builds and owns the measurement and modeling that power Zoomph's broadcast and media intelligence products. The role combines hands-on data science with media measurement expertise, designing the frameworks that quantify viewership, brand and sponsor exposure, and ad impact across live broadcast and streaming. Working closely with the broadcast product, computer vision, and engineering teams, the role turns raw detection and audience data into trustworthy, client-ready sponsorship valuation. This is a strong fit for a data scientist who wants to apply causal inference, statistical modeling, and machine learning to real-world media data in a fast-paced sports analytics SaaS environment.

Responsibilities

• Design and maintain measurement frameworks and statistical models that quantify viewership, engagement, and brand and sponsor exposure across broadcast and streaming.
• Develop causal inference and machine learning methods for outcome and ad-impact measurement.
• Validate models against real-world data and document methodology so results are defensible to clients.
• Partner with the broadcast product and computer vision teams to define and improve detection-quality and audience metrics.
• Build cross-platform identity, reach, frequency, and attribution methods that deduplicate audiences across screens.
• Monitor measurement accuracy and drive improvements with engineering and product.
• Build and maintain large-scale data pipelines and analyses on broadcast and streaming data.
• Translate complex analytical output into clear KPIs, narratives, and visualizations for product, sales, and clients.
• Analyze trends in viewership and sponsorship measurement to surface opportunities and gaps.
• Work with product and go-to-market teams to define KPIs and shape how Zoomph positions its broadcast measurement.
• Enable sales and customer success on measurement methodology through clear documentation.

Job Requirements

• Bachelor's or Master's degree in Statistics, Computer Science, Data Science, or a related quantitative field.
• 3-5+ years of hands-on data science or advanced analytics experience in media, broadcast, or ad-tech.
• Proficiency in Python and/or R, with experience in SQL and cloud data platforms (AWS, GCP, or Azure).
• Experience with statistical modeling, causal inference, or machine learning applied to audience or campaign data.
• Strong communication skills, with the ability to translate analytical findings into business recommendations.
• Experience with cross-screen or TV viewership measurement methodologies is strongly preferred.
• Familiarity with media attribution, reach/frequency modeling, or TV ad impact measurement is preferred.
• Background in identity graph analytics or cross-platform audience deduplication is preferred.
• Prior work at a media measurement, streaming analytics, or sports data company is a strong plus.

We are authorized to hire in the following states: Arizona, California, Connecticut, Illinois, Maryland, Massachusetts, Missouri, New York, North Carolina, South Carolina, Tennessee, Texas, and Virginia.

Zoomph EEO Statement: "We examine our unconscious biases and take responsibility for always striving to create an inclusive environment that makes every employee and applicant feel welcome. Zoomph is committed to providing a workplace free from discrimination or harassment. We expect every member of the Zoomph community to do their part to cultivate and maintain an environment where everyone has the opportunity to feel included and is afforded the respect and dignity they deserve. Decisions related to hiring, compensation, training, evaluating performance, or termination are made fairly, and we provide equal employment opportunities to all qualified candidates and employees and prohibit all forms of employment discrimination, including harassment, because of race, color, sex, gender, age, religion or religious creed, national origin, ancestry, citizenship, marital status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military or veteran status, or any other characteristic protected by law."