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Overview

We are seeking a Data Analytics, Data Science, and AI Lead to serve as a unified strategic capability for the organization. This role sits at the intersection of business insight, advanced analytics, and applied AI, shaping how data and intelligence drive decisions, experiences, and operational excellence across the organization. This leader is both a strategist and a builder - setting a clear vision, developing high-performing talent, and delivering measurable impact - while remaining close enough to the work to guide technical direction, architectural decisions, and the practical application of analytics and AI at scale. Experience in the media/entertainment industry is a strong advantage, or other creative industries. The ideal candidate has experience building Data Science solutions to support, not replace, human judgment, and therefore has experience optimizing for results beyond just accuracy improvements, such as enriching insights and discussions.

Responsibilities

• Set the vision and roadmap for Data Analytics, Data Science, and AI, aligned to business priorities and growth strategy.
• Lead and scale high-performing teams across data science, applied AI/ML, experimentation, and data analytics.
• Drive advanced analytics and AI use cases including personalization, forecasting, optimization, experimentation, generative AI, and decision intelligence.
• Own data analytics strategy - ensuring accurate, trusted measurement across web, mobile, and emerging digital experiences, and translating signals into action.
• Bridge insight to execution by partnering closely with business stakeholders, product, engineering, operations, and executive leadership.
• Evaluate and govern platforms and tooling, balancing build vs. buy decisions, cost, scalability, and long-term flexibility.
• Establish responsible AI and data practices, including model governance, experimentation standards, and measurement of business impact.
• Tell compelling data stories - turning complex analyses and models into clear narratives that drive confident decisions at the executive level.

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) and SQL fluency is a plus.
• Familiarity with AWS tools, especially SageMaker, or equivalent cloud-based ML environments.

Education

MS/BS in Computer Science