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Staff Data Scientist - Verification & Validation

Staff Data Scientist designs statistical frameworks and conducts robust analysis to validate safety-critical AI systems for autonomous vehicles. Delivers data-driven insights to engineering teams and leadership, scaling pipelines for petabyte-scale driving data analysis. Requires MS/PhD in quantitative field and expertise in Python, SQL, and statistical methods.

256k – 307kFoster City, CABoston, MAData ScienceHybrid

About the role

Responsibilities

  • Design evaluation frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.
  • Conduct robust analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
  • Inform strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
  • Define metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
  • Lead the lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
  • Scale pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.

Qualifications

  • MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
  • Proficiency in Python and SQL with experience in production-quality code
  • Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis
  • Experience with large-scale data analysis and statistical modeling
  • Proficiency with Git, unit testing, and collaborative development practices

Bonus Qualifications

  • Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines
  • Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks
  • Experience with designing metrics and delivering actionable insights that drive business decisions

Skills

PythonSQLGitBayesian InferenceHypothesis TestingSparkSpark SqlDatabricksSpatiotemporal ModelingMultivariate Analysis

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