Sr Staff Data Architect-18692

Northrop Grumman Northrop Grumman · Aerospace · Roy, UT +1 · Data Science

Senior Staff Data Architect/Data Scientist role focused on designing and building scalable data platforms and pipelines for AI/ML initiatives within a defense program. The role involves translating data strategy into executable designs, ensuring data trustworthiness and usability, and championing advanced analytics methods while defining standards for model validation and delivery.

What you'd actually do

  1. Translate business the Chief Data Officers Data and AI Strategy into an executable design
  2. Design the data platform that makes data trustworthy, usable, and scalable across the SDS division
  3. Build resilient big‑data pipelines (Spark, Flink, Snowflake, etc.) that meet security classifications, privacy regulations, and governance standards.
  4. Champion modern statistical, machine‑learning, and deep‑learning methods (e.g., Bayesian inference, causal‑impact analysis) to solve high‑visibility problems and deliver competitive advantage.
  5. Define and enforce standards for model validation, reproducibility, observability, and continuous delivery of analytic products.

Skills

Required

  • Python
  • R
  • Scala
  • Spark
  • Flink
  • Snowflake
  • Databricks
  • Qlik
  • Cloudera
  • Hadoop
  • Palantir Foundry
  • Microsoft Fabric
  • IBM Watsonx.data
  • Denodo
  • Data Modeling
  • Cloud environments
  • Hybrid cloud environments
  • Digital Threads
  • AI/ML data pipelines
  • Bayesian inference
  • Causal-impact analysis

Nice to have

  • Thought Leadership & Vision
  • Problem Solving Innovation
  • Technical Leadership
  • Relocation assistance

What the JD emphasized

  • Knowledge, Skills and Ability LRSP/AOP critical skill usually demonstrated.
  • security classifications, privacy regulations, and governance standards
  • model validation, reproducibility, observability, and continuous delivery of analytic products

Other signals

  • Translate business the Chief Data Officers Data and AI Strategy into an executable design
  • Design the data platform that makes data trustworthy, usable, and scalable across the SDS division
  • Build resilient big‑data pipelines (Spark, Flink, Snowflake, etc.) that meet security classifications, privacy regulations, and governance standards.
  • Champion modern statistical, machine‑learning, and deep‑learning methods (e.g., Bayesian inference, causal‑impact analysis) to solve high‑visibility problems and deliver competitive advantage.
  • Define and enforce standards for model validation, reproducibility, observability, and continuous delivery of analytic products.