Senior Applied Scientist

Microsoft•Redmond, WA

1d•$119,800 - $234,700

About The Position

As a Senior Applied Scientist, you will solve problems, look at problems through a different lens, and work closely with customers to innovate new solutions to complex problems. You will identify trends and provide unique business solutions. The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports our most complex and leading-edge customer engagements. This role drives early-stage deliveries, enhances ISD’s technical capabilities, and partners with others to develop approaches, innovative solutions, and engineering standards to set sales and delivery teams up for success. Leveraging the principles of model, care, and coach, we provide consistent high-quality customer experience through technical and AI leadership and IP capture centered on delivery truth.

Requirements

Bachelor'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 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.

Nice To Haves

Experience with agentic frameworks and orchestration, agentic retrieval/search (multi-step, adaptive re-search) for enterprise environments, and systematic evals using rubrics + quantitative metrics to measure and improve agent quality end-to-end.
3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
Experience developing AI Agents.
Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
3+ years experience conducting research as part of a research program (in academic or industry settings).
1+ year(s) experience developing and deploying live production systems, as part of a product team.
1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
Publications in top tier AI conferences like CVPR/ACL/ICML/NIPS.
Experience in customer engagement and large language model post training.

Responsibilities

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.
Brings new technology and approaches into production by applying long-term research efforts to solve immediate product needs.
Collaborates with and bridges the gap between researchers (in community across the company, Microsoft Research [MSR], or in their own organizations) and development teams.
Negotiates across teams to ensure cutting edge technology is being applied to products in a practical way that meets key business objectives.
Develops an understanding of research approaches used across a group or organization to leverage (and not re-invent) solutions.
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 or service.
Designs an approach to solve significant business problems shared by a senior team member.
Masters one or more subareas (e.g., Object Recognition, Text Classification) and gains expertise in a broad area of research (e.g., Machine Learning, Natural Language Processing, Computer Vision, Statistical Modeling, Data-Driven Insights).
Understands the corresponding literature and applicable research techniques.
Uses expertise to identify the right technique to use when examining a problem.
Serves as an expert within product domain.
Gains deep knowledge in a complex or highly ambiguous service, platform, or domain.
Shares knowledge of changes in industry trends and advances in applied technologies with engineers and product teams to apply advanced concepts to identify product needs and drive action toward solutions.
Fosters audience for the product based on understanding of the industry.
Reviews business and product requirements and incorporates state-of-the-art research or previously tested solutions occurring at Microsoft and the academic field to formulate plans that will meet business goals.
Identifies problems and develops strategy to resolve team or feature level problems.
Provides strategic direction for the kinds of data used to solve problems.
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.
Applies deep subject matter expert knowledge around several specialized tools/methods to support business impact.
Provides mentorship by participating in onboarding to less experienced team members (e.g., interns, research associates) and guiding less experienced team members in processes, scenarios, projects, and their careers, and provides guidance around best practices and standards.
Assists in developing academics to be members of multi-discipline teams.
Identifies and inspires peers and new research talent to join Microsoft.
Participates in candidate screening and interviewing and forms job descriptions for attracting new talent.
May share research findings through publications or industry outreach.
Collaborates with the academic community to develop the recruiting pipeline, identify cutting-edge solutions for products, and establish awareness of their work.
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.
Leverages data analysis knowledge to clean, transform, analyze, integrate, and organize data to the level required by the analysis techniques selected.
Develops usable 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.
Mentors and coaches less experienced members in data cleaning and analysis best practices.
Identifies gaps in current datasets and drives onboarding of new datasets (e.g., bringing on third-party datasets).
Attempts to fix bugs in data to inform developers how to improve the products.
Ensures representative data to honor problem definition and ethics.
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.
Provides guidance to less experienced team members.
Uses data pipelines for training, as well as for shipping models which should execute correctly.
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).
Identifies possible machine learning formulations that map to the problem and selects the formulation that gives the optimal outcome (e.g., predicting the actual age or age group).
Leverages state-of-the-art algorithms that structures, analyzes, and uses data in products and platforms to train algorithms scalable for artificial intelligence solutions before deploying.
Uses familiarity of machine learning frameworks (e.g., uses open source libraries) to train algorithms.
Collaborates and helps less experienced team members through process.
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.
Mentors less experienced team members through modeling processes.
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.
Addresses models that break during production (e.g., due to input streams changing).