Applied Scientist 2 (Ads)
Microsoft
Applied Scientist 2 (Ads)
Beijing, China
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Overview
With rapid growth of advertising market share by the state-of-the-art online advertising platform and service, Microsoft Ads algorithm team is at the core of this effort, working on the following research & development: Selection(recall), Relevance, User Response Prediction (Click Prediction and Conversion prediction), Auto-bidding, Large Language Model and Large-scale Machine Learning & Serving System. The team is a world-class R&D team of passionate and talented scientists and engineers who aspire to solve challenging problems and turn innovative ideas into high-quality products and services that can help hundreds of millions of users and advertisers for direct business impact.
As an Applied Scientist in Microsoft Ads team, you will focus on algorithm improvements and collaborate across different feature teams to deliver the impact for business growth. This opportunity will allow you to accelerate your career growth, develop deep business acumen, and become expert in certain algorithm area. Under the company policy guidance, there is a flexible work opportunity for you to work from home partially).
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.
Qualifications
- 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.
- Solid background on machine learning or LLM related techniques
- Rich experience in ads or recommendation system recall and ranking modeling.
- Self-motivated with good communication, collaboration, and analytical skills.
- Pass CET-6 and fluent in English speaking and writing
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.
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.