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Boeing

Builds commercial aircraft, defense and space systems

Experienced Data Scientist

Applied Machine LearningPosted on Nov 10
Full-timeDallas, TX, USA$106k - $162kIn-person

About the Job

Requirements

  • At least 4 years of experience in data science.
  • At least 2 years of experience with Python.
  • At least 2 years of experience with databases and querying using SQL to extract and aggregate data.
  • At least 5 years of experience in data visualization, including creating clear charts and dashboards.
  • At least 3 years of experience with model evaluation, including metrics, cross-validation, bias/variance trade-offs, and calibration.
  • The position requires a U.S. Person as defined by 22 C.F.R. §120.62 to meet U.S. export control compliance requirements.

Responsibilities

  • Envision, develop, and implement optimized supply chain solutions to enhance support and services under Boeing Global Services’ Distribution portfolio.
  • Use optimization techniques to evaluate stocking strategies, safety stocks, supplier capacity constraints, economic order quantity, and related factors.
  • Apply analytics technologies to customer-facing software tools, products, and services, using machine learning, statistics, and other analytics techniques for aviation and aerospace challenges.
  • Contribute data science expertise to cross-functional teams developing, launching, and maintaining aviation software solutions.
  • Design, implement, and operate data pipelines, machine learning models, backend services, frontend dashboards, and integrations supporting SIOP and fleet/entity analytics.
  • Build and productionize forecasting, demand-sensing, and inventory/fleet optimization models, and expose them through robust APIs and user interface components.
  • Implement telemetry ingestion and preprocessing for fleet telematics, ERP/MRP feeds, maintenance systems (EAM/CMMS), and external market and competitive data.
  • Create interactive scenario-planning tools and visualizations for SIOP and program decision reviews, enabling what-if simulation and explainability of model outputs.
  • Implement role-based access, data lineage, and governance for sensitive enterprise datasets, and ensure data quality and reproducibility.
  • Containerize, deploy, and monitor model and application services using continuous integration and continuous delivery, observability, and automated testing.
  • Implement model monitoring, drift detection, retraining pipelines, and feature stores for the production machine learning lifecycle.
  • Collaborate with stakeholders to translate business questions into technical solutions and measure business impact, including forecast accuracy, inventory reduction, and fleet readiness.
  • Mentor engineers and analysts and establish best practices for reproducible notebooks, modular code, testing, and documentation.

Desired Qualifications

  • Knowledge of the aviation and aerospace industry.
  • Experience with model deployment, including APIs, Docker, and MLOps pipelines.
  • Knowledge of deep learning, time series forecasting, natural language processing, and causal inference.
  • Cloud certification, such as AWS certification.

About the Company

Boeing

Builds commercial aircraft, defense and space systems

Boeing builds commercial airplanes, military aircraft and space systems for airlines, governments and other aerospace customers worldwide. Its passenger jet families include the 737 and 787, while its defense portfolio spans fighter aircraft, helicopters, tankers and satellites. Engineers design and integrate aircraft structures and systems, and factories assemble and test the finished products for delivery to customers. The company earns money through aircraft and equipment sales, government development contracts, and services such as maintenance, spare parts and flight training. Boeing was founded in 1916 and is headquartered in Arlington, Virginia.

Company size
10,001+
Company stage
IPO
Headquarters
Arlington County, VA
Founded
1916

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