Principal Applied Scientist

** Overview*
* Conversational commerce introduces challenges that differ from traditional web shopping. Preferences emerge through dialogue, expectations for accuracy and trust are high, and systems must reason over context and frequently changing commerce data.

Microsoft Copilot is building shopping experiences that are conversational, proactive, and trustworthy. As a Principal Applied Scientist, you will lead the development of machine learning and generative AI systems that power product discovery, ranking, personalization, and reasoning across Copilot shopping surfaces. This role sits at the intersection of applied machine learning, generative AI, and product experience, with clear ownership of core shopping intelligence used directly in user-facing Copilot experiences.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.  

** Responsibilities*
* + Design, build, and product ionize machine learning models for product discovery, ranking, recommendation, and personalization using large-scale commerce and behavioral data.

+ Develop LLM-based systems for conversational shopping, including prompt design, retrieval-augmented generation, tool orchestration, and grounding against structured commerce data.

+ Address quality and trust challenges such as hallucination risk, stale data, and recommendation reliability.

+ Define evaluation frameworks and experimentation strategies for conversational and proactive shopping scenarios, including offline metrics and online experiments.

+ Partner closely with product, engineering, and design teams to translate models into low-latency, reliable Copilot experiences.

+ Provide technical leadership for applied science within Copilot Shopping through design reviews, mentoring, and setting quality standards.

+ Contribute to model governance and Responsible AI practices to ensure trustworthy and compliant systems.

** Qualifications*
* *
* Required Qualifications:

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* + 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.

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* Preferred Qualifications:

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* + Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)

+ OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)

+ OR equivalent experience.

+ 3+ years of hands-on experience developing machine learning or statistical models to solve real-world problems (in industry or academic projects), including building and validating algorithms such as regressions, classifiers, or clustering models.

+ Proficiency in programming for data science (e.g. using Python or R for data analysis and modeling) and experience with data querying languages (e.g. SQL).

+ Big Data & Distributed Computing:
Hands-on experience with large-scale data processing using tools like Apache Spark or Azure Databricks for training and inference workflows.

+ Advanced Analytics:
Skilled in time-series analysis and anomaly detection techniques (e.g., ARIMA, isolation forests) applied to business contexts for actionable insights.

+ LLMs & Domain Adaptation:
Practical experience with prompt engineering, fine-tuning…