Principal Data and Applied Scientist

What you'll do

• Translate ambiguous business questions into well-scoped scientific problems with clear hypotheses and measurable objectives.
• Own the statistical strategy for measuring AI impact using causal and experimental frameworks like A/B testing.
• Define and govern metric frameworks that connect AI usage to downstream outcomes like productivity and revenue.
• Develop ML and GenAI models using advanced techniques to quantify incremental value to the business.
• Lead end-to-end evaluation of GenAI solutions, including diagnosing performance issues and recommending fine-tuning.
• Write robust, reusable code and analytical pipelines to ensure impact measurement is repeatable at scale.
• Communicate findings and trade-offs to senior leadership in clear business language to influence investment decisions.
• Raise the scientific bar by providing design reviews, mentorship, and reusable measurement standards for the team.

What we're looking for

• Bachelor's degree plus 6 years of experience, Master's plus 4 years, or Doctorate plus 3 years in a quantitative field.
• Equivalent experience to the specified educational and experience requirements is acceptable.
• Experience in Python, PySpark, SQL, and Large Language Models (preferred).
• Experience with ML development platforms such as Azure Machine Learning, Azure AI Foundry, and Azure OpenAI (preferred).
• Ability to write and maintain declarative Spark code and manage operational aspects like cluster sizing and memory (preferred).
• Experience in Azure Synapse or Databricks (preferred).
• 6+ years of experience creating publications, such as patents or peer-reviewed academic papers (preferred).