Boeing is seeking an Experienced Data Scientist-Applied Artificial Intelligence (AI) Developer to join the Finance Systems and Analytics (FS&A) team to design and implement AI-enabled analytics solutions for Finance, delivering prescriptive and predictive analytics and developing advanced capabilities such as agentic AI workflow automation.
Requirements
- 2+ years of hands‑on experience applying AI/Machine Learning (ML) to business problems, including production or near‑production deployments of Large Language Models (LLMs)-enabled systems, Retrieval-Augmented Generation (RAG) pipelines, or multi-agent workflows
- Design, build, and operate agentic AI workflows or orchestrated agent networks that perform agent-to-agent reasoning and integrate with programmatic finance interfaces
- Have practical experience with cloud platforms (Amazon Web Services (AWS)/Google Cloud Platform (GCP)/Azure), containerization (Docker/Kubernetes), and MLOps tooling (Continuous Integration and Continuous Delivery (CI/CD), monitoring, feature stores)
- Have hands‑on experience with vector databases, retrieval‑augmented generation, prompt/context engineering, and LLM safety/mitigation strategies
- 3+ years of hands‑on experience in data analytics, statistical analysis, or applied machine learning on business/finance problems
- Experience in Python or R for data analysis, model development, and scripting
- Experience with SQL skills and experience querying relational databases (e.g., SQL Server, Oracle, Teradata) or cloud data warehouses
Responsibilities
- Design, prototype, and productionize AI solutions for finance outcomes (e.g., reconciliation, forecasting, anomaly detection, decision support), including RAG systems and multi-agent orchestration
- Own the full model lifecycle: source and validate finance data, build features, train and evaluate models/agents, and implement CI/CD pipelines for repeatable deployments and rollbacks
- Implement and maintain agent orchestration, state management, and inter‑agent reasoning; optimize for latency, cost, and reliability when integrating with finance Application Programming Interfaces (API) and transactional systems
- Implement human‑in‑the‑loop controls, escalation logic, and audit trails to ensure explainability, auditability, and compliance with internal controls and regulations
- Design and operate monitoring and observability for models and agents (performance metrics, drift detection, logging, alerting); lead incident response and retraining strategies
- Perform exploratory and multivariate analysis to discover patterns, validate assumptions, and translate findings into clear business recommendations
- Collaborate closely with Finance SMEs, Data Engineering, Security, and Compliance to ensure solutions meet business needs, scalability, and governance standards
Other
- Possess domain knowledge in Finance (e.g., accounting, treasury, reconciliation, payments, Financial Planning and Analysis (FP&A)) or equivalent business-facing experience to partner effectively with Finance Subject Matter Experts (SMEs)
- Communicate complex technical risks and results clearly to non-technical stakeholders and leadership
- Work effectively in collaborative engineering environments using modern practices (code reviews, feature branches, cross-functional coordination, agile practices, cross-functional coordination, agile delivery)
- Be familiar with model governance, auditability, and regulatory controls relevant to financial models (e.g., model risk frameworks, SOX)
- Understand security, privacy, and data protection best practices for sensitive financial data
