Director, Data Science

Essential Duties And Responsibilities

• Define and drive the vision, strategy, and operating plan for Data Science across the organization, aligning initiatives to business priorities and revenue objectives including partnering on AI intiatives.
• Build and lead a high-performing team of Data Scientists, fostering a culture of innovation, accountability, and continuous development.
• Identify, prioritize, and execute high-impact use cases across personalization, forecasting, pricing, marketing optimization, and customer experience.
• Establish and track KPIs to measure the impact of data science initiatives on revenue, margin, customer engagement, and operational efficiency.
• Partner with executive leadership and cross-functional stakeholders to translate business needs into scalable data science & AI solutions.
• Oversee the design, development, and deployment of predictive models, and intelligent systems at enterprise scale.
• Ensure strong collaboration with Data Engineering and AI/ML Engineering teams to productionize models through robust architecture, MLOps, and scalable infrastructure.
• Standardize data science best practices, including experimentation frameworks, model governance, documentation, and reproducibility.
• Help evaluate and implement new tools, technologies, and vendor solutions to enhance AI/ML capabilities.
• Communicate complex analytical insights and model outcomes to executive and non-technical audiences, enabling data-driven decision making.
• Continuously assess and optimize data science operations to improve efficiency, scalability, and business impact.

Qualifications

• PhD or Master's degree in Computer Science, Statistics, Mathematics, or related field.
• Proven experience leading data science teams and driving strategic initiatives.
• Experience in building and deploying machine learning and AI models at scale.
• Strong understanding of data engineering principles and practices.
• Excellent communication and presentation skills, able to convey technical concepts to non-technical stakeholders.
• Experience with data science methodologies, including data exploration, feature engineering, model selection, and evaluation.
• Knowledge of data privacy regulations and best practices.