Data Science Vice President

Role Description The Data Science Vice President at GLENCORE DRC is a full-time remote role responsible for setting the vision, strategy, and execution of data science initiatives across the organization. This role oversees the design and deployment of advanced analytics solutions, guides teams in building predictive and prescriptive models, and ensures that data-driven insights are effectively translated into business actions. Day-to-day activities include leading and mentoring data science and analytics professionals, prioritizing projects in partnership with senior leadership, and establishing standards for data governance, model quality, and ethical AI use. The role also includes collaborating with cross-functional teams, communicating complex analytical findings to non-technical stakeholders, and continuously evaluating new tools, methodologies, and technologies to enhance data capabilities. The Data Science Vice President is expected to drive measurable impact on operational efficiency, risk management, and strategic decision-making through rigorous analytical leadership.
Qualifications
Strong Analytical Skills and Pattern Recognition capabilities to interpret complex datasets and identify meaningful trends.
Advanced proficiency in Data Science and Data Analytics, including experience with machine learning frameworks and data visualization tools.
Solid foundation in Statistics for experimental design, hypothesis testing, and robust model evaluation.
Demonstrated experience leading data science teams, setting strategic direction, and delivering impactful analytics solutions at a senior leadership level.
Proficiency with programming languages commonly used in data science (such as Python or R) and familiarity with big data ecosystems and cloud platforms.
Excellent communication and stakeholder management skills, with the ability to explain complex concepts clearly to non-technical audiences.
Experience in large-scale industrial, commodities, or resource-focused environments is beneficial.
Master’s or PhD in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.

Work arrangement
Yes