Experienced Data Scientist-Applied Artificial Intelligence Developer

Experienced Data Scientist-Applied Artificial Intelligence Developer

Company:

The Boeing Company

The Boeing Company is currently seeking an Experienced Data Scientist-Applied Artificial Intelligence (AI) Developer to join the Finance Systems and Analytics (FS&A) team in Tukwila, WA. 

The candidate selected for this role will design and implement AI-enabled analytics solutions for Finance. You will deliver prescriptive and predictive analytics and help develop advanced capabilities such as agentic AI workflow automation. In this position you will design and build networks of interdependent autonomous agents that perform agent to agent reasoning, integrate with internal programmatic interfaces, streamline finance workflows, support human in the loop decision making, and optimize transactional processes.

The ideal candidate will:

• Have 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

• 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)

• 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)

• Understand security, privacy, and data protection best practices for sensitive financial data

• Have hands‑on experience with vector databases, retrieval‑augmented generation, prompt/context engineering, and LLM safety/mitigation strategies

Position 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

Basic Qualifications (Required Skills/Experience):

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience

• 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

• Experience with statistical methods, risk analysis, and applying t