Senior Applied Scientist · Microsoft

Fri, 24 Apr 2026 12:12:25 GMT
Engineering

Microsoft · Redmond · hybrid

Are you ready to make a global impact that has visible impact on consumers? Join Bing Multimedia AIGC (AI Generated Content) team at Microsoft, where we experiment, learn and deliver rich creation experiences to consumers worldwide.

As part of Microsoft AI, our team is taking a giant step in advancing and incorporating AI into our search experience, delivering new and exciting AI creation features with state-of-the-art models. There is no limit to creativity in our work, at a scale and reach that is unmatched.

As a Senior Applied Scientist in Bing Multimedia AIGC team, you will help us to build and improve AIGC experiences on Bing, that are extensible and usable on other Windows products, including Copilot. You will research and develop an understanding of metrics, tools, data workflows, hill-climbing methods used to measure AIGC’s success metric, and deep analysis of our product quality to inform strategic business directions.

Together with our business owners, you will be challenged with defining the best business measurement methodology to prove a working business model for how Search and Creations integrate as one product. You will also have the opportunity to fine-tune optimizations for our creation models.

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:

Leverages data analysis knowledge to clean, transform, analyze, integrate, and organize data to the level required by the analysis techniques selected.

Develops useable datasets for modeling purposes.

Scales the feature ideation and data preparation.

Takes cleaned or raw data and adapts data that for machine learning purposes.

Uses understanding of which features are important that come out of the model and identifies the optimal features.

Identifies gaps in current datasets and drives onboarding of new datasets.

Works with team to optimize signal system design.

Identifies gaps in current datasets and drives onboarding of new datasets (e.g., bringing on third-party datasets).

Leverages or designs and uses machine learning/data extraction, transformation, and loading (ETL) pipelines (e.g., data collection, cleaning) based on data prepared and guides team to do so.

Influences the direction of the team.

Establishes the pipeline so that the team can conduct all of their experiments and data processing.

Uses data pipelines for training, as well as for shipping models which should execute correctly.

Establishes collaborative relationships with relevant product and business groups inside or outside of Microsoft and provides expertise or technology to create business impact.

Takes initiative and drives activities such as technology transfers attempts, standards organizations, filing patents, authoring white papers, developing or maintaining tools/services for internal Microsoft use, or consulting for product or business groups.

May publish research to promote receiving new intellectual property for business impact.

Independently works to create product impact.

Identifies approach, and applies, improves, or creates a research-backed solution (e.g., novel, data driven, scalable, extendable) to positively impact a Microsoft product.

Designs an approach to solve significant business problems shared by a senior team member.

May publish research to promote receiving new intellectual property for product impact.

Performs documentation of work in progress, experimentation results, plans, etc.

Documents scientific work to ensure process is captured.

Creates informal documentation and may share findings to promote innovation within group or with other groups.

Uses deep understanding of fairness and bias.

May contribute to ethics and privacy policies related to research processes and/or data/information collection by providing updates and suggestions around internal best practices.

Seeks to identify potential bias in the development of products.

Helps address scalability problems by adjusting to stakeholder needs.

Works with large-scale computing frameworks, data analysis systems, and modeling environments to improve models.

Applies the model to real products, and then verifies effects through iterations.

Experiments by putting multiple models in production and evaluating their performance.

Continues to monitor how algorithm performs against expected behaviors and performance or accuracy guardrails.

Monitors over time for input and output data that there are changes over time.

Uses system to run analyses on an ongoing basis such as by comparing predicted value with actual value.

Collaborates with others and helps lead others to leverage data to identify pockets of opportunity to create state-of-the-art algorithms to improve a solution to a business problem.

Consistently leverages knowledge of techniques to optimal analysis using algorithms.

Identifies opportunity areas regarding new statistical analyses and drives solutions.

Uses statistical analysis tools or modifies existing tools for evaluating Machine Learning models and validates assumptions about the data while also reviewing consistency against other sources.

Runs basic descriptive, diagnostic, predictive, and prescriptive statistics.

Represents the team’s insights.

Characterizes the customer’s problem through metrics to measure the quality of machine learning systems.

Calibrates metrics to support decision making for data (e.g., gaining awareness of ideal metrics and use of metrics).

Qualifications:

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) OR Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)