Data & Applied Scientist II

Our mission centers on solving the core problem of computational advertising: selecting an optimized slate of relevant ads that maximizes a comprehensive utility function encompassing expected revenue, user experience, and advertiser return on investment.

As a world-class R&D team of passionate scientists and engineers, we are dedicated to addressing these challenges with innovative ideas and turning them into high-quality products and impactful solutions. We empower hundreds of millions of users to find what they need while enabling advertisers to reach their ideal audiences, creating a seamless marketplace experience that drives success across the board.

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

Response and Resolution:

• Leverages understanding of data science and business to examine a project and consider factors that can influence final outcomes within a technical area. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Documents key business objectives. Effectively communicates business goals in analytical and technical terms. Consistently shares insights with stakeholders.
• Build and maintain production-level machine learning models to assess and predict the relevance between ads and diverse user contexts, such as search queries or conversational interactions. Employ cutting-edge techniques, including large language models (LLMs) and state-of-the-art innovations from academia and industry, to enhance relevance modeling and drive impactful outcomes. Utilize Python, PyTorch and open-source libraries to train and fine-tune large language models. Apply advanced techniques like transfer learning, domain adaptation, and prompt engineering to tailor pre-trained LLMs to specific advertising scenarios. Build efficient training pipelines, inference pipelines for offline and online serving on production environments.

Readiness:

• Understands where to acquire data necessary for successful completion of the project plan. Utilizes querying, visualization, and reporting techniques to describe acquired data, including format, quantity, identities, and other surface properties. Explores data for key attributes and contributes to the development of data quality report describing results of the task, initial findings, and impact on the project. Collaborates with others to perform data-science experiments using established methodologies, statistics, optimization, and probability theory for general purpose software and statistical packages. Assesses different tools and techniques and selects the appropriate one. Serves as an effective partner in data preparation efforts to Solution Architects, Consultants, and Data Engineers. Adheres to Microsoft's privacy policy related to collecting and preparing data. Identifies data integrity problems.
• Derive meaningful insights and generate hypotheses from massive datasets using a variety of advanced techniques such as machine learning, feature engineering, statistical modeling, and data mining. Leverage methods like regression, classification, natural language processing (NLP), optimization, and p-value analysis to solve complex problems effectively.

Product/Process Improvement:

• Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing [NLP], image recognition) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, autoregressive integrated moving average [ARIMA], recurrent neutral networks [RNN], long short-term memory [LSTM] networks) to identify the