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# Director, Data Science & AI

- **Company:** Trusted Media Brands
- **Location:** Milwaukee, WI, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Information Engineering, Python (Programming Language), Microsoft Copilot, SQL Databases, Google Cloud, Machine Learning Operations, Claude, Google Gemini, Software Coding, GPT
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/director-data-science-ai-milwaukee-wi--9a1aefad-0626-4582-bba3-b0b3a5dc71f8

## About the Role

Data Science, Artificial Intelligence (AI), Entertainment and Media, Social Media, Licensing, Marketing Software, Data Science, Automation, Artificial Intelligence (AI), Team Lead/Manager, * 7+ years in data science or analytics, including 3+ years leading a team

 * A track record of data science work that changed business decisions or outcomes, and you can tell us what moved and by how much
 * Experience partnering directly with non-technical leaders (marketing, content, sales, product) to scope problems and drive adoption
 * Comfort building from imperfect data: you've led data cleanup, modeling foundations, and quality work, not just analysis on top of it
 * Strong Python and SQL, plus a solid foundation in statistics, experimentation, and forecasting
 * Practical experience applying AI and LLM tools (such as Claude, Copilot, ChatGPT, and Gemini) to real workflows
 * A bias for action

Nice to have:

 * Hands-on experience with Google Cloud Platform (BigQuery, Vertex AI), including deploying models or agents in production
 * Experience with media, content, or audience data

## Description

TMB is looking for a Director of Data Science & AI to lead the team turning our data into better business decisions, build data products that drive personalized experiences and real outcomes, and apply AI and automation where they genuinely help. You understand how business users think, what friction looks like in real workflows, and how to communicate technical work in ways that land with non-technical partners.

Your primary mission is to bridge the gap between what's technically possible and what's practically useful. This is a hands-on leadership role: you'll set direction, but you'll also be in the data, in the model reviews, and building alongside your team when it counts. A big part of the job right now is foundational. You'll partner closely with Data Engineering to get our data clean, structured, and trustworthy, then build on it with practical data science, from content scoring to forecasting to experimentation. If you're energized by building that foundation and turning it into tools people actually use, this role is for you.

You are a fast learner who is comfortable in ambiguity, energized by solutioning, and motivated by seeing your work drive real adoption across the organization. You'll spend your time championing smarter ways of working, and helping both technical and non-technical teammates integrate AI into their everyday work, not as a disruption, but as a natural extension of how they already operate.

Responsibilities: your day-to-day:

 * Own the data science roadmap, from statistical modeling, experimentation, forecasting, and segmentation to applied AI, prioritized by where it adds real business value
 * Lead, mentor, and stay hands-on with a small team of data scientists: writing code and reviewing methodology, not just roadmaps
 * Partner with Data Engineering to get our data clean, well-structured, and trustworthy enough to build on, and own the data science side of that foundation
 * Build data products that improve how we evaluate, acquire, and program content, such as content scoring and performance prediction
 * Oversee the design of production-grade solutions on our cloud stack, choosing the simplest approach that solves the business problem
 * Champion the adoption and continuous improvement of AI tools across both technical and non-technical teams
 * Establish practical data quality, governance, and responsible AI standards as our work scales
 * Spend real time with business teams to understand how decisions get made today, then find where data and AI can make them better
 * Collaborate cross-functionally with Business Intelligence, Data Engineering, Analytics Engineering, Development, and Product teams to drive technical innovation, prioritize initiatives, ship MVPs fast and iterate based on usage data
 * Design and run rigorous experiments to validate hypotheses and guide business decisions
 * Translate statistical and technical work into business language for executive stakeholders and make the case for where data science investment should go next
 * Define success metrics for internal tools and AI initiatives, and track adoption, usage, and business impact over time
 * Document tools, workflows, and best practices to scale impact beyond individual engagements
 * Stay informed on industry trends, viral content, and emerging platforms to identify new opportunities