At MNTN, we put our people first, full stop. This allows our company culture to be defined by our team members and their shared values, like trust, ambition, quality, radical honesty, and compassionate leadership. It’s why we all really love working for the Hardest Working Software in Television™ (and also why we were named one of Ad Age’s Best Places To Work in 2025.)

We pride ourselves on bringing unrivaled performance and simplicity to Connected TV advertising. Our self-serve technology makes running TV ads as easy as search and social, helping brands drive measurable conversions, revenue, site visits, and more. It’s what led MNTN to being named one of Fast Company's Most Innovative Companies in 2023. You can learn more about us and everything we do by visiting https://mountain.com/.

We’re committed to innovation that empowers, not replaces. At MNTN, AI is a tool for growth, enhancing efficiency while keeping a people-first approach. Our goal is to streamline workflows and drive new solutions—without compromising the human element that makes our company great.

So if wanting to do more, own more, and make a bigger impact comes naturally to you, then you may be the person we're looking for to join us in our next stage of growth.

We’re looking for a Senior Data Scientist to help shape MNTN’s sovereign identity data backbone powering targeting, bidding, measurement, and cross-device attribution for Performance TV marketing. In this role, you’ll develop the methodologies, models, and graph-based approaches that unify identity signals across fragmented data sources and improve the accuracy, scalability, and interpretability of identity resolution. This is a hands-on role for someone excited by large-scale data science, AI-assisted development, and deep research in graph-based entity resolution.

What you’ll do

Design and improve graph-based approaches for identity resolution across devices, households, and identifiers which improve match quality, coverage, and stability across a rapidly changing identity landscape.
Use Scala, Spark, SQL, and cloud-native tools to analyze large identity datasets, build models, and productionize data science workflows.
Contribute production-grade code to shared repositories, using strong engineering practices to build clear, scalable, and maintainable systems.
Define validation strategies and measure model performance and business impact on targeting, measurement, and attribution.
Help shape the team’s approach to identity science and partner across Engineering, Product, and Analytics to deliver production-ready solutions.
Leverage LLMs, AI editors (Cursor, Copilot, Claude Code), and agentic workflows to accelerate research, prototyping, documentation, testing, and iteration.
Apply privacy-by-design principles to ensure identity science work is auditable, compliant, and aligned with governance standards.

What you’ll bring

5+ years of experience in data science, machine learning, or applied research working with large-scale datasets in production.
Strong experience building identity graphs, entity resolution systems, record linkage pipelines, or related graph-based matching systems.
Deep expertise in Scala, Spark, SQL, and/or Python for distributed processing, model development, and experimentation.
Strong foundation in applied statistics, machine learning, graph algorithms, clustering, probabilistic matching, and model evaluation.
Experience productionizing data science solutions in partnership with engineering, including testing, monitoring, and reproducibility.
Experience mentoring data scientists and helping define technical direction across a team.
Comfortable with AI-assisted workflows and modern development tools, including LLMs.
Deep ownership mindset - you care about correctness, explainability, scalability, observability, and maintainability.
Entrepreneurial, customer-first mindset - you connect identity science quality to marketing performance and attribution accuracy.

Nice to Have

Experience in adtech, martech, measurement, attribution, or privacy-sensitive consumer data environments.
Hands-on experience with Google Cloud Services and cloud-native data and ML tooling such as BigQuery, Dataproc, GCS, and Kafka.
Experience with graph algorithms and techniques such as label propagation, connected components, community detection, graph embeddings, and/or link prediction.
Familiarity with privacy-enhancing technologies, data governance practices, and evolving identity standards.

Senior Data Scientist at MNTN