AI Applied Scientist II

Microsoft•Redmond, WA

5d•$100,600 - $199,000

About The Position

Do you want to build a product that blends the cutting edge of Microsoft AI, the Microsoft 365 & Microsoft Teams ecosystem, and the Power Platform to empower every seller on the planet to achieve more? Are you energized by fast-paced, collaborative teams using diverse technologies to deliver real value quickly? If so, you'll feel right at home on the Microsoft Sales Agent team. The emergence of AI technologies and digital collaboration is disrupting the ways in which selling is happening. Instead of navigating Customer Relationship Management (CRM) platforms, sellers now expect AI-powered agents, and insights directly integrated into their day-to-day productivity tools to get real time coaching. The Microsoft Sales Agent team brings autonomous agents which are intelligent and powers contextual experiences into the modern-work suite across Microsoft Copilot, Outlook, and Teams. These experiences seamlessly integrate with leading Customer Relationship Management (CRM) systems like Salesforce or Dynamics 365. As a AI Applied Scientist for The Sales Agent Team, you will play a pivotal role in advancing Microsoft's mission to empower every individual and organization on the planet to achieve more. You will contribute to the development and integration of cutting-edge AI technologies into Microsoft products and services, ensuring they are inclusive, ethical, and impactful. You will collaborate across product, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and AI to solve complex problems. Your work will directly influence product direction and customer experience. We are in an era of unprecedented innovation and openness. As Microsoft continues to lead in AI, we are seeking individuals to help tackle some of the most exciting and meaningful challenges in the field. Our vision is to build a truly open architecture platform that enables users to summon tailored AI agents to drive real-world outcomes. We are looking for an AI Applied Scientist II to join our team. This role will combine AI knowledge with applied science expertise and demonstrate a growth mindset and customer empathy. Join us in shaping the future of AI agents. 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.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Nice To Haves

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow).
Experience developing and deploying live production systems one or more of the following: C#, Java, React/Angular, TypeScript.
Experience with design and implementation of enterprise-scale services.
1+ years of experience publishing in peer-reviewed venues or filing patents.
Experience presenting at conferences or industry events.
1+ years of experience conducting research in academic or industry settings.
1+ years of experience working with Generative AI models and ML stacks.
Experience across the product lifecycle from ideation to shipping.

Responsibilities

Bringing the State of the Art to Products
Collaborates with and bridges the gap between researchers (in community, Microsoft Research [MSR], or in their own organizations) and development teams. Brings new technology and approaches into production by applying long-term research efforts to solve immediate product needs.
With limited guidance from others, 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 or service. Solves components or aspects of a problem as assigned by a senior team member. May publish research to promote receiving new intellectual property for product impact.
Participates in collaborative relationships with relevant product and business groups inside or outside of Microsoft and provides expertise or technology to create business impact. Participates in technology transfer attempts, 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.
Capability Management and Networking
Maintains ties with external network of peers and identifies prospective talent, when asked. May contribute to publications on research findings. May participate in candidate interviews. Collaborates with the academic community to develop the recruiting pipeline and establish awareness of their work.
Reinforces a positive environment by applying best practices. May support mentorship by assisting with onboarding of research interns or other entry-level team members, if applicable.
Documentation
Performs documentation of work in progress, experimentation results, plans, etc. Documents scientific work to ensure process is captured. Participates in the creation of informal documentation and may share findings to promote innovation within group.
Ethics and Privacy
Understands and follows ethics and privacy policies when executing research processes and/or collecting data/information.
Leveraging Applied Research
Applies strategy by understanding the role in the team and applying the strategy provided by senior team members and incorporates state-of-the-art research. Asks probing questions to better understand strategy.
Gains expertise in one or more subareas of research (e.g., Object Recognition, Text Classification), gains understanding of a broad area of research (e.g., Machine Learning, Natural Language Processing, Computer Vision, Statistical Modeling, Data-Driven Insights), and understands the corresponding literature and applicable research techniques. Uses understanding of approaches to identify techniques and seeks feedback from senior team members.
Researches and develops an understanding of tools, technologies, and methods being used in the community that can be utilized to improve product quality, performance, or efficiency. Contributes knowledge around several specialized tools/methods to support the application of business impact or serves as an expert in a deeply specialized area.
Gains deep knowledge in a service, platform, or domain and acquires knowledge of changes in industry trends and advances in applied technologies. Consults with engineers and product teams to apply advanced concepts to product needs. Learns product domain by reviewing products.
Machine Learning Functionality, Insights, and Technical Tools
Prepares data to be used for analysis by reviewing criteria that reflect quality and technical constraints. Reviews data and suggests data to be included and excluded. Describes actions taken to address data quality problems. Assists with the development of useable datasets for modeling purposes. Supports the scaling of feature ideation and data preparation. Helps take cleaned data and adapts for machine learning purposes, under the direction of a senior team member. Seeks guidance from senior team members when confronted with problems/challenges.
Uses machine learning algorithms that structures, analyzes, and uses data in product and platforms to train algorithms for scalable artificial intelligence solutions before deploying. Begins to develop new machine learning improvements independently while under the direction of a senior team member.
Collaborates to leverage data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem. Uses statistical analysis tools for evaluating Machine Learning models and validating assumptions about the data while also reviewing consistency against other sources. Begins to independently run basic descriptive, diagnostic, predictive, and prescriptive statistics. Assists with the communication of insights under the direction of senior team members.
Supports the application and use of intelligence created during the training of algorithms for deployment. Seeks information about large-scale computing frameworks, data analysis systems, and modeling environments to improve models. Helps create a model, apply the model to real products, and then verify effects through iterations. Helps with experiments by putting multiple models in production and evaluating their performance. Sets up monitoring and implementation to track production models, under the direction of a senior team member. Addresses models when that break, under the direction of others.
Leverages or designs and uses machine learning/data extraction, transformation, and loading (ETL) of pipelines (e.g., data collection, cleaning) based on data prepared.