Responsibilities
Build and operate a data-driven prioritization framework for CADET quality forums and partner teams that combines DSAT, customer impact, usage, severity, strategic importance, representation, and current evaluation coverage to guide quality investments.
Define and maintain intent, sub-intent, customer scenario, coverage, and loss-pattern taxonomies that can be used consistently across signal intake, triage, evaluation, and reporting.
Measure how well evaluation portfolios represent production traffic, customer segments, workflow complexity, locales, grounding paths, and material failure modes.
Identify underrepresented customers, intents, scenarios, and loss patterns, and translate those gaps into evaluation and data-collection priorities.
Design quality gates for top intents and top customers, including success thresholds, segmentation, run cadence, escalation criteria, and reporting.
Build recurring scorecards that connect offline evaluation movement with online measures such as DSAT, task completion, retries, abandonment, and escalation; detect meaningful quality changes; and alert accountable owners when action is required.
Analyze offline-online agreement, evaluation freshness, regression coverage, grader reliability, and quality movement over time.
Develop sampling, weighting, deduplication, clustering, and trend-detection approaches for noisy customer and product signals using resource- and performance-optimized data-analysis solutions that make effective use of CPU, GPU, and platform capacity.
Use causal and experimental methods where appropriate to distinguish correlation, attribution, and treatment impact, and translate the findings into concrete product, model, data, and evaluation investment decisions.
Partner to translate customer evidence into valid task distributions, datasets, metrics, and reward signals and to encode metrics, taxonomies, data-quality checks, and reporting into automated pipelines.
Leverage team signals from customer engagements to understand workflows, business impact, expected outcomes, and gaps hidden by aggregate metrics; produce clear recommendations for product, model, data, and evaluation investments; and communicate them to senior leaders.
Establish solid practices for data provenance, privacy, responsible use, reproducibility, and metric governance.
Qualifications
Required Qualifications:
Preferred Qualifications:
#cadets
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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