Technical Director IV - Data Analytics, Data Science, AI(no third party, only W2)

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, Google Cloud Platform, 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