Senior Data Scientist

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.

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.