Senior/Lead Data Scientist

Boeing•St. Louis, MO

1d•$216,000 - $250,000•Onsite

About The Position

Boeing Enterprise AI and Data is seeking a Senior/Lead Data Scientist to join a Data Science and Analytics team in the St. Louis, MO area to support enterprise business-critical outcomes across areas such as Manufacturing, Supply Chain Management and Aftermarket product support. This role will lead the development and deployment of high-impact predictive and prescriptive analytics and will shape analytics strategy, architecture, and technical direction across a portfolio of complex problems. The ideal candidate brings deep expertise in advanced analytics and machine learning, strong engineering and MLOps instincts, and the ability to influence senior stakeholders and cross-functional teams to deliver measurable business results.

Requirements

Bachelor’s degree or higher from an accredited course of study in data science, computer science, machine learning, applied statistics, mathematics, engineering, or related field.
10+ years of Data Science experience
10+ years of end-to-end analytics/ML solutions, including problem definition, data preparation, model development, validation, deployment, and monitoring.
10+ years experience in a position that requires analytical, quantitative reasoning and/or mathematical modeling skills.
10+ years of experience with Python and SQL.
10+ years of experience with machine learning/statistical modeling (e.g., regression, classification, clustering, time-series, anomaly detection, causal/experimental methods), including model evaluation and validation.
10+ years of experience with data visualization and decision support (e.g., Python, Tableau, Power BI, or equivalent) to communicate insights and drive adoption.
5+ years of experience working with cloud and/or enterprise analytics stacks and building production-ready solutions (e.g., Azure/AWS/GCP; Spark/Databricks; containerization and CI/CD patterns).
5+ years of leading technical work and mentoring other data scientists; demonstrated influence across cross-functional stakeholders; ability to communicate technical content in oral and written form.
US Secret clearance or ability to obtain one.

Nice To Haves

Experience supporting manufacturing, quality, safety, or supply chain analytics in an industrial environment.
Experience developing and deploying solutions using MLOps/DataOps practices (e.g., Git-based workflows, model registries, automated testing, monitoring, reproducible pipelines).
Experience with NLP/LLMs, computer vision, and/or graph methods applied to operational and engineering data.
Experience with optimization and simulation for prescriptive analytics and operational decision support.
Experience working with GPUs and computation clusters.
Strong track record of presenting technical recommendations and business cases to senior leadership.

Responsibilities

Leads the design, development, validation, deployment, and lifecycle management of end-to-end predictive/prescriptive analytics solutions (e.g., forecasting, anomaly detection, optimization, risk scoring, early-warning systems).
Owns problem framing with business and operational stakeholders; translates ambiguous needs into measurable objectives, success metrics, analytical requirements, and delivery roadmaps.
Selects best-fit methodologies (e.g., statistical modeling, machine learning, deep learning, NLP, computer vision, time series, simulation, optimization) and defines modeling approaches, evaluation strategies, and governance.
Drives data preparation and feature engineering for complex, multi-source datasets; establishes repeatable pipelines for data quality, lineage, and model inputs.
Establishes and enforces modeling and engineering standards (code quality, peer review, documentation, reproducibility, bias/robustness checks, monitoring, retraining triggers).
Leads technical reviews (design, algorithm, code, and model risk reviews) and provides guidance to other data scientists and partner teams.
Partners cross-functionally with analytics, engineering, quality, safety, operations, and product/IT teams to integrate solutions into business workflows and decision systems.
Influences analytics strategy for the organization, including platform/tooling recommendations, model deployment patterns, experimentation/measurement approaches, and reuse of common assets.
Monitors deployed solutions (performance drift, data drift, operational KPIs) and drives continuous improvement through iteration, retraining, and user feedback.
Mentors and develops junior data scientists; actively contributes to knowledge sharing, technical communities, and capability building across the organization.
Communicates complex technical outcomes clearly to senior leadership, including tradeoffs, risks, assumptions, and expected business impact.