Manager, Audience Architecture & Data Science

Overview

The Manager, Audience Architecture is the technical builder and platform operator within DTAI’s Audience Strategy practice. Where Audience Strategists define the what and why of audience approaches for clients, this role owns the how: building, deploying, and maintaining the data products, models, and technical infrastructure that bring those strategies to life. This person is equal parts artist and marketing technologist. They work across clean room environments, identity platforms, CDPs, and data collaboration tools to create scalable audience products for internal DTAI and IR teams.

They sit alongside Audience Strategists in client rooms as the technical authority, translating complex data concepts into clear, actionable language. The ideal candidate is a builder with agency-side fluency. They are highly motivated, entrepreneurial, and detail-oriented, with a keen understanding of the challenges brands face when building intelligence on their customers. They write production-quality code, design audience models, operate comfortably across multiple data platforms, and can explain what they built to a CMO without losing them.

They bring a startup mentality and a bias toward shipping over theorizing.

• Audience Product Development & Data Science
• Design, build, and maintain custom audience models (propensity scoring, lookalike modeling, custom segmentation, clustering) using statistical and ML techniques
• Develop and deploy internal audience tools and data products that increase the speed, accuracy, and scalability of the Audience Strategy team’s output
• Operate across multiple clean room and data collaboration environments (Epsilon, Live Ramp, Snowflake, Habu, AWS Clean Rooms, retailer-specific environments) as a platform-agnostic technical resource
• Build automated pipelines for audience creation, refresh, and distribution across activation platforms
• Own the technical execution of Test & Learn roadmaps co-developed with Audience Strategists, from hypothesis design through measurement and iteration
• Evaluate and prototype emerging AI/ML applications for audience development, including generative AI use cases, predictive modeling enhancements, and automation of manual workflows

• Technical Client Partnership
• Serve as the technical voice in client-facing meetings alongside Audience Strategists, translating data science concepts and platform capabilities into business-relevant language
• Provide consultative guidance on clean room readiness, first-party data strategy, identity architecture, and data collaboration best practices
• Advocate for deterministic identity solutions and advanced data use cases, including partnership with investment teams to support campaign activation
• Perform audience analysis to extract actionable insights and inform strategic activation recommendations
• Communicate platform opportunities and technical constraints clearly to both technical and non-technical stakeholders

• Platform & Infrastructure Enablement
• Serve as the primary technical operator across DTAI’s audience and identity technology stack, maintaining working fluency in each platform’s capabilities, limitations, and integration points
• Document technical processes, data flows, and platform configurations to build institutional knowledge and reduce key-person dependency
• Identify and recommend platform improvements, new tool integrations, and process optimizations to the Audience Strategy and DTAI leadership teams
• Partner with Advanced Analytics and AdOps teams on cross-functional technical

• 4-6 years of experience in digital marketing, data science, or marketing technology, with at least 3 years in a hands-on data science or audience architecture role
• 2+ years of experience working in data lake and/or clean room technologies
• Advanced proficiency in SQL and Python (Num Py, pandas, scikit-learn)
• Demonstrated experience with statistical modeling techniques: logistic regression, clustering (k-means, hierarchical), decision trees, propensity modeling, and dimensionality reduction
• Hands-on experience with at least two of the following: data clean rooms (Epsilon, Live Ramp, Habu, Snowflake, AWS Clean Rooms), CDPs…