Data Scientist (data Science)

Boeing · Aerospace · Seattle, WA +1

Boeing Defense, Space & Security is seeking a Mid-Level Data Scientist with experience in AI, ML, and Generative AI to join their team. The role involves developing and optimizing GenAI models, contributing to AI-driven applications, and working with deep learning frameworks and GPU infrastructure. Responsibilities include fine-tuning, evaluation, prompt engineering, prototyping, and collaborating with MLOps teams. Experience in regulated industries and with container technologies is preferred.

What you'd actually do

  1. Contribute to the development and deployment of models.
  2. Design and implement pipelines for model fine-tuning and evaluation.
  3. Develop prompt engineering strategies and embedding techniques to enhance model performance.
  4. Prototype applications that address specific business needs.
  5. Assist in model performance evaluation and bias/fairness assessments.

Skills

Required

  • Python
  • data engineering workflows
  • Spark
  • Airflow
  • SQL
  • Bachelor's degree in computer science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics or a closely related field
  • 3+ years of experience in data science or machine learning

Nice to have

  • Master's or PhD in Computer Science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics or a closely related field
  • fine-tuning open-source models
  • integrating APIs from commercial providers
  • PyTorch
  • TensorFlow
  • AI ethics and governance in generative models
  • data engineering tools

What the JD emphasized

  • GenAI
  • regulated industries
  • deep learning frameworks
  • GPU compute infrastructure
  • distributed model training
  • container technologies
  • fine-tuning
  • evaluation
  • prompt engineering
  • embedding techniques
  • model performance evaluation
  • bias/fairness assessments

Other signals

  • development and deployment of models
  • fine-tuning and evaluation
  • prompt engineering
  • embedding techniques
  • prototype applications
  • model performance evaluation
  • bias/fairness assessments
  • MLOps and engineering teams
  • model scaling and monitoring
  • AI/ML/GenAI tools and trends