Technical Director IV - Data Analytics, Data Science, AI

Burbank, CA (On-site)

Role: Technical Director IV - Data Analytics, Data Science, AI Location: Burbank, CA Required Qualifications: • 12+ years in analytics, data science, or AI, with significant leadership experience • A seasoned leader with deep experience in data science and applied AI, and a strong grasp of modern analytics ecosystems. • Comfortable operating at multiple altitudes—from board-level strategy to hands-on technical guidance. • Experienced in product and digital analytics, experimentation frameworks, attribution, and customer behavior analysis. • Fluent in ML concepts and architectures (predictive models, NLP, recommendation systems, generative AI) and how they create real business value. • Adept at building teams, culture, and operating models that scale across functions and geographies. • Pragmatic, impact-driven, and allergic to vanity metrics. • Strong foundation in statistics, machine learning, and experimentation. • Experience in retail, e-commerce, consumer products, or digital platforms. • Experience with cloud data and AI platforms (AWS, GCP, Azure) and modern analytics stacks. • Proven track record of driving measurable outcomes through data and AI initiatives. • Exposure to generative AI at scale and AI-enabled product development. • Prior ownership of company-wide analytics standards or AI governance. 4–6 years of experience in data science. • Experience in media & entertainment industry is a strong advantage but not required • Curiosity and capability to work in an experimental stage of development to test hypotheses and adjust approaches to deliver the most value to business users • Experience building predictive models, especially with limited sample sizes • Understanding of clustering and dimensionality reduction techniques • Exposure to generative AI models (e.g., LLMs, diffusion models) and an interest in applying them to real-world data problems • Strong communication skills and the ability to translate data science work into business value as well as translate business user needs into data science • Strong skills in Python and common ML libraries (e.g., scikit-learn, XGBoost, pandas). SQL fluency is a plus • Familiarity with AWS tools, especially SageMaker, or equivalent cloud-based ML environments