Data Science Manager, Model Audit

- Direct and supervise a team focused on assessing controls related to model development, validation, lifecycle governance, data governance, data quality, and related processes - Oversee the execution of complex audits, including scoping, planning, fieldwork, issue development, and reporting; ensure compliance with quality standards and professional guidance - Collaborate with senior Internal Audit leadership and cross-functional teams to identify emerging risks and technology trends that impact control environments - Transform enterprise risk priorities into focused audit objectives and risk-based plans for data and model risk, ensuring consistency with regulatory standards - Assess the adequacy and effectiveness of controls throughout the model lifecycle and data management practices - Mentor teams of data scientists and analysts to develop advanced statistical models, algorithms, and techniques that address business challenges - Spearhead enhancements in data science practices by introducing best practices for model development and validation across teams - Partner with senior leadership to define strategic objectives, set key performance indicators (KPIs), and prioritize projects that yield actionable insights - Clearly communicate complex data-driven insights to various stakeholders, fostering understanding and engagement - Manage assorted teams and specialized units for resource planning and ensuring successful execution of modeling initiatives - Analyze large and intricate datasets to derive actionable insights and present findings effectively to stakeholders - Review intricate code and analyses to ensure precision, scalability, and ethical compliance - Collaborate with data engineering and IT teams to enhance data quality and governance practices continuously - Ensure the proficient use of advanced statistical methods and oversee model lifecycle governance - Create and manage budgets for initiatives, ensuring efficient resource allocation in line with organizational objectives - Educate teams on innovative data science methodologies and promote an environment of exploration and inquiry - Investigate, assess, and incorporate new data science technologies and methods to bolster the teams capabilities and encourage innovation