Principal Data Scientist, Marketing Analytics

Serve as the technical leader for Marketing Analytics, helping define the roadmap for marketing measurement, advanced analytics, automation, and AI use cases. Design and develop scalable analytics solutions, data products, and automation frameworks that improve marketing decision-making. Translate complex business challenges into technical solutions and analytical approaches, balancing rigor with practical execution. Define and implement best practices for marketing analytics engineering, data science, experimentation, AI adoption, and solution development within Marketing Analytics. Directly manage and mentor a Marketing Data Science Analyst, providing technical leadership, coaching, and career development support. Promote knowledge sharing, reusable approaches, and best practices across Marketing Analytics and enterprise Data Science. Help shape team processes and operating models that enable efficient execution and scalable delivery without duplicating enterprise-level capabilities. 8+ years of experience in Data Science, Analytics Engineering, Marketing Analytics, Data Engineering, or related technical disciplines. Advanced proficiency in Python and SQL; experience building scalable analytical workflows and technical solutions. Experience working with cloud-based data environments and modern data platforms such as Snowflake, Databricks, AWS, Azure, or GCP. Experience developing APIs, data integrations, data models, and reusable analytics assets. Experience applying machine learning, statistical modeling, experimentation, and predictive analytics techniques to business problems. Strong experience working with customer, marketing, media, or consumer data. Demonstrated ability to translate business needs into scalable technical solutions and influence cross-functional stakeholders. Ability to explain technical concepts and analytical findings clearly to senior business leaders and non-technical audiences. Marketing measurement and attribution methodologies, including incrementality testing, causal measurement, and Marketing Mix Modeling (MMM). Retail media, digital media, marketing technology, or customer data ecosystems. Clean room technologies such as Epsilon, LiveRamp, Habu, InfoSum, or similar. Identity resolution, customer data platforms, or audience activation data flows. AI, Generative AI, MLOps, or machine learning operationalization. Experience supporting marketing, media, consumer insights, or customer-focused business functions. Marketing Analytics has a clear technical roadmap and scalable approach for advancing measurement, data integration, automation, and AI-enabled analytics. Marketing-specific analytics priorities are delivered more quickly while remaining aligned with enterprise standards and best practices. Internal teams have more reliable, accessible, and usable marketing data assets to support decision-making. Advanced measurement and AI use cases move from concept to practical application with clear business value. Enterprise Data Science, Technology, and Marketing Analytics teams view the role as a collaborative bridge that accelerates shared outcomes. Work across platforms and technologies including Snowflake, cloud environments, marketing technology systems, identity solutions, and clean room ecosystems. Improve accessibility, reliability, automation, and usability of marketing data assets that support measurement and decision-making. Develop advanced analytical solutions to evaluate marketing effectiveness across paid, owned, and earned channels. Apply methodologies such as experimentation, incrementality testing, attribution, customer analytics, forecasting, predictive modeling, and marketing mix analysis. Develop proof-of-concepts and scalable solutions; partner with technology teams to ensure solutions are secure, sustainable, and aligned with enterprise standards. Working in isolation from Enterprise Data Science, Technology, or Data Engineering teams.