Data Science Vice President

Role Description This full-time remote role of Data Science Vice President is responsible for leading the overall data science strategy, execution, and team leadership across the organization. Day-to-day responsibilities include overseeing the design and implementation of advanced analytics solutions, guiding the development of predictive models, and ensuring data-driven decision-making across business units. The role involves collaborating with executive leadership to identify high-impact opportunities, prioritizing data science initiatives, and communicating insights and recommendations in a clear, actionable manner. The Data Science Vice President will mentor and grow a diverse team of data professionals, establish best practices for data governance and model lifecycle management, and promote innovation in tools, techniques, and methodologies. This position also requires close collaboration with engineering, product, and operations teams to integrate models into production systems and measure their business impact.

Qualifications

Strong analytical skills with demonstrated ability to translate complex problems into structured, data-driven solutions. Deep expertise in data science and data analytics, including experience developing and deploying predictive and prescriptive models. Advanced knowledge of statistics and pattern recognition techniques applicable to large and complex datasets. Experience leading and managing data science teams, including hiring, coaching, performance management, and career development. Proficiency with modern data science tools and programming languages (e.g., Python, R, SQL) and familiarity with cloud-based data platforms. Track record of partnering with senior leadership to define strategy, influence decisions, and deliver measurable business outcomes. Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders and present to executive audiences. Background in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering, or related discipline); advanced degree is beneficial. Experience with machine learning operations (MLOps), data governance, and ethical use of data and AI is a plus. Ability to work effectively in a remote environment, demonstrating high levels of accountability, collaboration, and inclusivity.