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Senior Data Scientist - M365 Copilot, Agents & Platform - Turing Team

Redmond, WA, US; Mountain View, CA, US

Pay
Pay not listedSimilar roles state $118K–$193K
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
Apply at Microsoft

What you’ll work on

Full posting
  • Build representative evaluation datasets across enterprise and consumer scenarios

  • Design studies and rubrics for response-quality side-by-side evaluations

  • Prepare and analyze large-scale datasets in Python, SQL, KQL or similar

  • Build decision-relevant metrics, dashboards and narratives for product decisions

  • Mentor peers through analytical code reviews and study-design guidance

From the employer’s posting
Build representative evaluation datasets across enterprise and consumer scenarios
Contribute to response-quality side-by-side evaluations by designing studies and rubrics, analyzing results
Write efficient, readable code in Python, SQL, KQL, or similar languages to prepare and analyze large-scale datasets
Develop decision-relevant metrics, dashboards, and compelling narratives that translate analyses into actionable product recommendations
Provide mentorship through analytical code reviews, study-design guidance, innovation, and sharing data science best practices

Tools in this posting

  • Python
  • R
  • SQL
Source — Tool mentions in context
Data Analysis & Evaluation Dataset Development Write efficient, readable code in Python, SQL, KQL, or similar languages to prepare and analyze large-scale datasets, including customer feedback, text, and consented multimodal logs. Build representative evaluation datasets across enterprise and consumer scenarios, including information retrieval, text creation and editing, image and file workflows, shopping, education, coding, technical support, health, sports, news, and finance. Design evaluation coverage for text and voice inputs, single- and multi-turn conversations, and location- or geography-sensitive prompts. Partner with engineering, product management, and applied science teams to define measurement strategies and inform product decisions. Contribute to response-quality side-by-side evaluations by designing studies and rubrics, analyzing results, and assessing statistical and practical significance. Develop decision-relevant metrics, dashboards, and compelling narratives that translate analyses into actionable product recommendations. Stay current with AI offerings and competitive developments, using data and evaluation results to identify feature gaps and opportunities. Provide mentorship through analytical code reviews, study-design guidance, innovation, and sharing data science best practices. Embody our culture and values. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) These requirements include but are not limited to the following specialized security screenings: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) * OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. 4+ years of experience using Python, SQL, KQL, R, or similar languages to analyze large-scale structured and unstructured datasets. Experience driving effective cross-organizational communication, end-to-end analytical problem solving, collaboration, customer focus, and the ability to navigate ambiguity. Demonstrated ability to communicate technical details clearly across organizational boundaries. Demonstrated ability to work end to end in complex analytical problem domains, from problem definition and study design through execution, interpretation, and communication of results. Customer Obsession: Customer- and metrics-driven approach to evaluation, experimentation, and product decision-making. Growth Mindset: Demonstrated ability and motivation to learn, enter new domains, and manage through ambiguity.

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Actively hiringFirst seen by us 10 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 19, 2026 · 10 days ago
Recorded sightings
70
Last seen by us
Oct 29, 2026

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Job description

View original posting ↗

Data Analysis & Evaluation Dataset Development Write efficient, readable code in Python, SQL, KQL, or similar languages to prepare and analyze large-scale datasets, including customer feedback, text, and consented multimodal logs. Build representative evaluation datasets across enterprise and consumer scenarios, including information retrieval, text creation and editing, image and file workflows, shopping, education, coding, technical support, health, sports, news, and finance. Design evaluation coverage for text and voice inputs, single- and multi-turn conversations, and location- or geography-sensitive prompts. Partner with engineering, product management, and applied science teams to define measurement strategies and inform product decisions. Contribute to response-quality side-by-side evaluations by designing studies and rubrics, analyzing results, and assessing statistical and practical significance. Develop decision-relevant metrics, dashboards, and compelling narratives that translate analyses into actionable product recommendations. Stay current with AI offerings and competitive developments, using data and evaluation results to identify feature gaps and opportunities. Provide mentorship through analytical code reviews, study-design guidance, innovation, and sharing data science best practices. Embody our culture and values. Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) These requirements include but are not limited to the following specialized security screenings: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) * OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. 4+ years of experience using Python, SQL, KQL, R, or similar languages to analyze large-scale structured and unstructured datasets. Experience driving effective cross-organizational communication, end-to-end analytical problem solving, collaboration, customer focus, and the ability to navigate ambiguity. Demonstrated ability to communicate technical details clearly across organizational boundaries. Demonstrated ability to work end to end in complex analytical problem domains, from problem definition and study design through execution, interpretation, and communication of results. Customer Obsession: Customer- and metrics-driven approach to evaluation, experimentation, and product decision-making. Growth Mindset: Demonstrated ability and motivation to learn, enter new domains, and manage through ambiguity.
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Redmond, WA, US; Mountain View, CA, US

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