Principal Data and Applied Scientist

Partner with senior business, engineering, and product stakeholders to translate ambiguous questions about AI impact into well-scoped scientific problems with clear hypotheses, measurable objectives, and agreed definitions of success. Own the scientific and statistical strategy for measuring the business impact of AI, including the causal and experimental frameworks (A/B testing, quasi-experimental designs, counterfactual and uplift methods) used to separate real impact from correlation. Define and govern the metric framework that connects AI adoption and usage to downstream business outcomes such as productivity, revenue, retention, and cost, and set the standards for how those metrics are computed, validated, and interpreted across the organization. Turn problem formulations into executable plans by selecting or creating the appropriate methods, algorithms, and tooling, and by delivering results that are statistically valid, reproducible, and defensible under executive scrutiny. Write robust, reusable, and extensible code and analytical pipelines that make impact measurement repeatable at scale rather than a one-time analysis. Develop ML and GenAI models using advanced statistical, machine learning, and LLM techniques, and quantify their incremental value to the business. Lead the evaluation of GenAI solutions end to end, designing evaluation methodology, diagnosing quality and performance issues, identifying root causes, and recommending fine-tuning, reinforcement learning, or system-level fixes. Communicate findings, tradeoffs, and levels of confidence to senior leadership in clear business language, and influence investment and prioritization decisions based on the evidence. Raise the scientific bar across the broader team through design reviews, mentorship, and reusable measurement standards that other data scientists can build on. Use AI-powered tools in your daily work to accelerate coding, analysis, experimentation, and reporting. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Experience in Python, PySpark, and SQL and LLMs. Experience with ML development platforms such as Azure Machine Learning, Azure AI Foundry + Azure OpenAI. Experience in Spark and ability to write/maintain/understand declarative Spark code and to cleanly write and maintain SQL. Experience with the operational aspects of Spark such as setting optimal cluster size, executor memory, number of executors etc. and experience in Azure Synapse/Databricks (or similar). Experience in ML development platforms such as Azure Machine Learning, Azure AI Foundry + Azure OpenAI. 6+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).