Data Science and Analytics Senior Manager (Virtual)

About the position The Boeing Company has an exciting opportunity for a Data Science and Analytics Senior Manager with the Corporate Audit Leadership Development Program. The selected candidate will virtually support the Corporate Audit's Strategic Business Hubs. Our Strategic Business Hubs are located across the United States in: Renton, WA; Seattle, WA; Everett, WA; Berkeley, MO; Hazelwood, MO; and Plano, TX. As a Data Science and Analytics Senior Manager, you will be a member of Boeing's Corporate Audit Data Analytics Team, a national team covering a wide range and scope of audit activities. The selected candidate will assist Corporate Audit in accomplishing its objectives by bringing a systematic and disciplined approach to evaluate and improve the effectiveness of Boeing's governance, risk management and internal control environment by providing oversight to the team's data and automation support to perform independent evaluations of the adequacy and effectiveness of Boeing's production, finance, operations, compliance and technology functions.

Responsibilities

• Oversee a team of data scientists, architects, and engineers that work primarily on projects focused on Artificial Intelligence (AI) and Amazon Web Services (AWS) initiatives.
• Operate using modified Scrum framework that enables management of its many needs and demands.
• Develop a roadmap and lead the design and execution of the roadmap's vision and goals.
• Contribute to architecture decisions and facilitate cross-team collaboration to maximize operational efficiency and stability.
• Ensure effective communication, dependency management, and tracking of cross-team projects and needs.
• Foster a culture of excellence through code-to-production service ownership.
• Oversee, grow, and develop a team of data scientists and engineers focusing on DevOps culture, communication, engineering excellence, and career development.

Requirements

• Bachelor's degree or higher.
• 5+ years of experience managing projects with engineers and data professionals, focusing on innovation, quality management, and analytics in natural language processing and machine learning on unstructured data.
• Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R).
• Experience with AWS resources, configuration, deployment, and architecture.
• Experience with problem-solving and communication skills, with the ability to explain complex technical concepts to business stakeholders.
• Experience with data preparation methods and machine learning algorithms; experienced in the complete data science process from discovery and cleaning to model selection, validation, and deployment.
• Experience in developing tools using Large Language Models (LLMs), with skills in restructuring, refactoring, and optimizing code for efficiency.
• Experience working with and organizing cross-functional teams at all levels.

Nice-to-haves

• Bachelor's degree or higher in a quantitative field (e.g. statistics, operations research, information systems, bioinformatics, economics, computational biology, computer science, mathematics, physics, chemistry or similar quantitative fields).
• 1+ years of experience with AWS.
• Experience in aerospace manufacturing operations and/or manufacturing support.
• Experience working in technology, manufacturing, or finance.
• Experience with leading high-performing analytics teams.
• Experience with command-line scripting, data structures, and algorithms.
• Experience working in a role that develops Data Science solutions as part of a cross-functional team that includes Data Engineers, Product Owners, and Business Intelligence Analysts.
• Experience working in highly regulated environments (e.g., aerospace, defense, financial services).
• Experience utilizing a diverse array of technologies and tools as needed to deliver insights, such as fluency in Python.
• Experience writing production level code.

Benefits

• Competitive base pay and variable compensation opportunities.
• Health insurance.
• Flexible spending accounts.
• Health savings accounts.
• Retirement savings plans.
• Life and disability insurance programs.
• Paid and unpaid time away from work.
• Tuition reimbursement through the Learning Together program.

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