Senior Manager, Analytics - Applied AI

dentsu•Chicago, IL

40d•$113,000 - $182,850•Hybrid

About The Position

Senior Manager, Analytics - Applied AI Merkle Analytics Applied AI Practice Merkle is a leading data-driven, technology-enabled, global performance marketing agency that specializes in the delivery of unique, personalized customer experiences across platforms and devices. Our Analytics Applied AI Practice is at the forefront of implementing cutting-edge artificial intelligence solutions, with particular expertise in knowledge extraction and insights generation systems that transform how our clients engage with their structured and unstructured data. We also help clients deeply understand AI system performance through rigorous evaluation frameworks and continuous improvement cycles. The Senior Manager, Analytics - Applied AI is a critical leadership role within our growing practice. This position sits at the intersection of technical credibility, delivery leadership, and client engagement for complex AI systems. You will be responsible for leading the delivery of production-grade AI solutions, including conversational analytics, agentic workflows, and AI evaluation programs, while positioning clients for long-term ownership of the systems we build together. We are looking for a technically credible leader who combines deep Analytics DNA with handson AI experience and the ability to lead small teams through client engagements. This role sits within our innovation arm, so we're looking for someone who is innovative and researchoriented and comfortable turning emerging approaches into practical client solutions. You'll work closely with clients and our technical team to translate business requirements into scalable, reliable AI systems. This role requires someone equally comfortable whiteboarding architecture with engineers and communicating technical concepts to non-technical leaders.

Requirements

7+ years in analytics, data science, or related fields with at least 3 years focused on AI/ML systems, preferably including knowledge extraction, conversational analytics, or insights generation implementations
Track record leading small teams or workstreams through technical delivery, whether as a tech lead, engagement manager, or senior practitioner who guided junior team members
Demonstrated ability to communicate technical concepts to non-technical audiences. You can explain how an AI system generates insights to a marketing executive and why evaluation metrics matter to non-technical leaders.
Deep familiarity with cloud data platforms. Stack agnostic but specific experience in Databricks (Unity Catalog, Genie) or Snowflake (Cortex, Intelligence) strongly preferred.
Hands-on experience building LLM-based applications including prompt engineering, RAG implementations, and natural language interfaces for structured and unstructured data
Strong SQL proficiency and experience working with complex enterprise data models. You understand what makes semantic layers work.
Consulting, agency, or client-facing delivery experience preferred. You're comfortable owning a client relationship and navigating ambiguity.
Bachelor's degree in a quantitative field (Computer Science, Statistics, Mathematics, Engineering, or related); Master's degree preferred
Strong Python skills and software engineering fundamentals
Expert-level SQL and experience with query optimization, data modeling, and semantic layer design
Familiarity with AI evaluation methodologies including accuracy measurement, reliability testing, and continuous improvement frameworks for LLM-based systems
Experience with cloud platforms (Snowflake, Databricks, AWS, Azure, GCP) and modern data infrastructure; comfort working across client tech stacks
Understanding of LLM capabilities/limitations, RAG architectures, and agentic frameworks
Proven ability to lead delivery teams and drive technical decisions while keeping clients informed and aligned
Excellence in technical storytelling and translating complex AI system behavior into business terms
Strong presentation skills with ability to engage executives and practitioners in the same conversation
Experience navigating ambiguity and making trade-off decisions in client-facing environments

Nice To Haves

Experience building evaluation frameworks and golden datasets for AI/LLM systems
Background in marketing analytics, customer insights, or business intelligence
Experience with both structured data (SQL, data warehouses) and unstructured data (documents, text) pipelines
Published work or presentations on AI/ML topics
Certifications in Databricks, Snowflake, or cloud platforms

Responsibilities

Lead the delivery of knowledge extraction and insights generation systems including conversational analytics, text-to-SQL solutions, unstructured data processing, and agentic workflows that unlock value from client data at scale
Own client relationships as the technical lead for complex AI engagements, translating business requirements into technical approaches and communicating progress, risks, and results to non-technical stakeholders
Lead AI evaluation engagements that help clients understand how their AI systems are performing, assessing accuracy, reliability, and business impact while building continuous improvement cycles
Guide the design and implementation of semantic layers and data architectures that enable accurate natural language interfaces across complex enterprise data environments
Lead small teams (2-5 practitioners) through the full delivery lifecycle: requirements gathering, solution design, implementation, evaluation framework creation, and successful client handoff. Ensure clients are positioned to own and evolve the systems we build.
Establish and execute evaluation frameworks including golden datasets, accuracy metrics, quality benchmarks, and business impact measurement, both for systems we build and as standalone evaluation engagements for clients
Drive technical quality through architecture reviews, solution design guidance, and hands-on contribution where needed, while enabling your team to do the primary build work
Champion system transparency and explainability, ensuring clients deeply understand how AI systems make decisions and where limitations exist
Contribute to the technical roadmap for AI Analytics capabilities including evaluation of emerging technologies, proof of concepts for new approaches, and methodology development
Stay current with the evolving AI landscape, synthesizing research, whitepapers, and industry best practices into practical frameworks and approaches that advance our capabilities and benefit our clients
Represent the practice in client presentations, internal knowledge sharing, and thought leadership that positions Merkle's Analytics Innovation team as a leader in applied AI
Develop team members by providing technical mentorship, delivery coaching, and career guidance to AI practitioners on your engagements