Principal Data Scientist, Marketing Analytics

Keurig Dr Pepper•Burlington, MA

3d•$96,800 - $169,000

About The Position

We are seeking a Principal Data Scientist, Marketing Analytics, to serve as the dedicated technical leader within the Marketing Analytics team. This role will design and operationalize advanced analytics, measurement, data integration, and AI capabilities that enable smarter marketing decisions. This role will report to the Senior Director of Marketing Analytics and will partner closely with Enterprise Data Science to align on technical standards, leverage enterprise capabilities where appropriate, and ensure marketing-specific analytics priorities are delivered in a scalable and sustainable way. This role sits at the intersection of Marketing Analytics, Data Science, Data Engineering, and Artificial Intelligence. The ideal candidate combines deep technical expertise with strong business acumen and will partner closely with Marketing, Media, Technology, Data Management, Enterprise Data Science, and external partners to transform complex marketing data into scalable measurement solutions, analytics products, and actionable business insights. The role will directly manage a Marketing Data Science Analyst and help advance the technical capabilities, processes, and analytical solutions that support the Marketing Analytics function. Marketing Analytics is evolving from reporting and hindsight analysis to a more scalable decision system that connects data, technology, measurement, and AI. This role ensures the team has dedicated technical leadership to execute marketing-specific priorities while remaining aligned with enterprise data science standards, platforms, and best practices. As the dedicated technical lead within Marketing Analytics, this individual will shape analytical solutions, technical approaches, and scalable capabilities that help marketing leaders make better investment, audience, measurement, and optimization decisions.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
Partner with Technology, Data Engineering, and vendor teams to onboard, integrate, and operationalize new marketing, media, customer, and consumer data sources.
Lead technical implementation of APIs, direct data shares, clean room integrations, cloud-based data connections, and scalable analytics workflows.
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.
Identify opportunities to use AI, machine learning, and generative AI to improve marketing effectiveness, efficiency, insight generation, and decision support.
Develop proof-of-concepts and scalable solutions; partner with technology teams to ensure solutions are secure, sustainable, and aligned with enterprise standards.
Partner closely with Enterprise Data Science, Data Engineering, and Technology teams to align on technical standards, governance, architecture, and best practices.
Leverage enterprise platforms, tools, and capabilities while serving as the dedicated technical lead for Marketing Analytics priorities and initiatives.
Identify opportunities to apply, extend, and operationalize enterprise data science capabilities within marketing measurement, analytics, and AI use cases.
Act as a bridge between Marketing Analytics and Enterprise Data Science teams, helping align marketing priorities with broader enterprise analytics and data strategies.
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.