Data Science Director

The Director of Data Science sets the enterprise data science vision and leads the strategic application of advanced analytics, machine learning, and experimentation to drive measurable business outcomes across the organization. Partnering closely with executive leaders across Product, Technology, Advertising, Subscriptions, and the Newsroom, the Director ensures that data science initiatives are tightly aligned with the company's long-term business strategy and digital transformation goals.

This leader establishes and executes the enterprise data science roadmap, prioritizing initiatives that unlock value, mitigate operational risk, and create new opportunities for revenue growth and audience engagement. The Director is accountable for the overall impact, adoption, and effectiveness of data science solutions across the business, shaping how data informs decisions at every level of the company.

Responsibilities:

• Lead the Data Science function and roadmap by setting enterprise-level strategy, establishing priorities, and guiding project execution to deliver actionable insights, predictive models, and automated decision tools that directly support business growth and risk mitigation.
• Oversee and ensure the development and operationalization of machine learning and statistical models to improve customer understanding, strengthen acquisition and retention strategies, optimize advertising and subscription performance, and elevate decision-making for product and content teams. Remain hands-on as needed to review approaches, guide complex analyses, and unblock critical work.
• Advance analytics and measurement frameworks by designing experiments, attribution models, forecasting tools, and media/campaign analytics that enable business units to evaluate performance and achieve revenue and engagement targets.
• Collaborate with engineering, data platform, and product teams to define expectations, prioritize integration efforts, and ensure models, pipelines, and data products are deployed into production environments using cloud data platforms and modern MLOps practices, ensuring reliability, scalability, and responsible data governance.
• Define the vision and priorities for innovative data products by integrating newsroom data, user behavioral signals, and business datasets to support content strategy, newsroom operations, audience insights, and new digital revenue opportunities.
• Establish best-in-class standards for analytical rigor, model validation, experimentation, documentation, and reproducibility to ensure trustworthy, transparent, and ethical data use across the enterprise.
• Hire, train and guide the Data Science team through technical leadership, professional development, hiring strategy, and a culture of innovation and continuous learning, resulting in strong team performance and retention.
• Communicate complex analytical findings in accessible language to executives, business partners, and non-technical stakeholders to influence strategic priorities and resource allocation.
• Other duties as assigned.

• Bachelor's or advanced degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field.
• 10+ years of progressive experience in data science, machine learning, or advanced analytics including at least 5 years in a senior leadership role managing or directing data science teams within complex, data-driven organizations.
• Demonstrated success defining and executing enterprise-level data science strategy, aligning analytics priorities with organizational objectives, and delivering measurable business outcomes.
• Proven ability to prioritize and make trade-off decisions across multiple concurrent data science initiatives to maximize impact and resource efficiency.
• Experience leading the development, scaling, and operationalization of predictive models, experimentation frameworks, recommendation systems, or optimization tools that drive strategic business value.
• Strong background in data science operations and governance, including MLOps, model risk management, feature management, and responsible AI practices.
• Exceptional ability to influence and partner with senior executives, balancing technical depth with business acumen in strategic discussions and decision-making.
• Successful track record in building, mentoring, and scaling data science organizations, including managing talent strategy, organizational design, and performance management.
• Familiarity with cloud platforms (AWS, GCP, or Azure), modern ML frameworks, and large-scale data ecosystems with the ability to provide technical oversight rather than day-to-day execution.
• Experience serving as a strategic advisor or partner to C-suite executives, with demonstrated ability to align data science investments to corporate strategy.
• Excellent communication, narrative, and storytelling skills, with the ability to translate complex analytical insights into clear, actionable recommendations for executive and non-technical audiences.

• Advanced degree (Master's or Ph.D.) in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
• Proven experience leading enterprise data transformation, architecting data science strategies that deliver measurable business and financial outcomes.
  Leadership experience in data-driven product innovation, such as building personalization, recommendation, or automation systems that directly impact customer experience or revenue.
• Familiarity with responsible AI, model ethics, and data governance frameworks in regulated or compliance-driven industries.
• Experience standing up enterprise data platforms or ML infrastructure in partnership with technology and data engineering leaders.
• Demonstrated success driving cultural change toward data fluency and evidence-based decision-making at scale.
• Track record of building and scaling global or multi-domain data science organizations that integrate analytics across product, marketing, and operations.
• Experience in media, digital content, advertising technology, marketing analytics, or subscription-based businesses.
• Familiarity with NLP, generative AI, LLM fine-tuning, reinforcement learning, content classification models.
• Background in statistical experimentation at scale, A/B Testing, causal inference, and attribution modeling.