Data Science Vice President

Role Description This full-time remote role of Data Science Vice President is responsible for leading the organization’s data science vision, strategy, and execution. The role includes overseeing the design and implementation of advanced analytics solutions, guiding teams in developing models for forecasting, optimization, and pattern recognition, and ensuring data-driven insights inform key business decisions. The Vice President will collaborate with executive leadership and cross-functional partners to prioritize initiatives, define success metrics, and operationalize analytics outcomes. Day-to-day responsibilities involve reviewing model performance, mentoring data science professionals, setting standards for statistical rigor and data governance, and communicating complex findings in clear, actionable terms to technical and non-technical stakeholders. The role also includes evaluating new tools and technologies, managing budgets and resources for data initiatives, and fostering an inclusive, high-performance culture within the data science organization.

Qualifications

Demonstrated strength in Analytical Skills and Pattern Recognition to derive actionable insights from complex, high-volume data. Advanced proficiency in Data Science and Data Analytics, including model development, validation, and deployment in production environments. Solid grounding in Statistics, including experimental design, hypothesis testing, and probabilistic modeling. Experience leading data science or analytics teams at a senior level, with a track record of delivering measurable business impact. Proficiency with modern data and machine learning ecosystems (e.g., Python or R, SQL, cloud platforms, ML frameworks) and familiarity with data engineering concepts. Excellent communication and presentation skills, with the ability to translate complex technical topics into clear recommendations for executives and business partners. Strong strategic thinking, stakeholder management, and decision-making abilities, including experience setting roadmaps and priorities for data initiatives. Master’s or PhD in a quantitative field (e.g., Data Science, Statistics, Computer Science, Mathematics, Engineering, or related discipline) or equivalent practical experience. Experience working effectively in remote or distributed teams and promoting inclusive, collaborative work practices.