Applied Scientist II
Microsoft
Job Summary
The Applied Scientist II will contribute to the Product Ads Algorithm & Infrastructure team within Microsoft's Ads Understanding group. Responsibilities include R&D on intelligent search advertising systems, leveraging large-scale data to extract actionable insights. This role requires improving algorithms and models (especially using deep learning), analyzing performance, developing scalable solutions, and directly impacting user and advertiser experiences. The candidate will work with data from user queries, online activities, advertiser campaigns, and responses within the Bing Ads ecosystem. The goal is to enhance revenue and improve both user and advertiser experiences.
Must Have
- Bachelor's/Master's/PhD in relevant field
- 2+ years experience in NLP/CV with deep learning
- 2+ years coding experience (C++, C#, Java, or Python)
- Analyze performance & identify opportunities
- Develop & deliver robust and scalable solutions
Good to Have
- PhD in relevant field
- Publications in ML, NLP, CV
- Online advertising experience
- Experience in parallel/distributed processing
- Experience with LLMs and Vision Transformers
Perks & Benefits
- Industry-leading healthcare
- Educational resources
- Product and service discounts
- Savings and investments
- Maternity and paternity leave
- Generous time away
- Giving programs
- Networking opportunities
Job Description
Overview
Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billions of a $600 billion advertising market already online. Search engines, web publishers, major ad networks, and ad exchanges are now serving billions of ad impressions per day and generating terabytes of user events data every day. Rapid online advertising growth has created enormous opportunities and technical challenges that demand computational intelligence. Computational Advertising has emerged as a new interdisciplinary field that involves information retrieval, machine learning, data mining, statistics, operations research, and micro-economics, to solve challenging problems that arise in online advertising. The central problem of computational advertising is to select an optimized slate of eligible ads for a user to maximize a total utility function that captures the expected revenue, user experience and return on investment for advertisers. Microsoft is innovating rapidly in this space to grow its share of this market by providing the advertising industry with the state-of-the-art online advertising platform and service. Microsoft Ads Relevance and Revenue (RnR) team is at the core of this effort, responsible for research & development of all the algorithmic components in our advertising technology stack, including:
- User/query intent (text and image) understanding, document/ad understanding, user targeting - Relevance modeling, IR-based ad retrieval
- User response (click & conversion) prediction using large scale machine learning algorithms - Marketplace mechanism design and optimization, and whole-page experience optimization - Personalization
- Innovative new ads products
- Network protection, fraud detection, traffic quality measurement
- Advertising metrics and measurement, including relevance and ad campaign effectiveness
- Data mining and analytics
- Supply-demand forecasting
- Ad campaign planning and optimization
- Experimentation infrastructure including tools for configuring and launching experiments, dashboard, live marketplace monitoring, and diagnosis.
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
Required 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.
- 2+ years working experience in statistical natural language processing (NLP) with latest deep learning technologies including transformer and LLMs
- OR 2+ years working experience in Computer Vision (CV) with latest deep learning technologies including Vision Transformers.
- 2+ years working experience with coding in production systems using c++, c#, Jaya or Python.
Preferred Qualifications:
- Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- Publication records in ML, NLP, CV areas.
- Experience in online advertising.
- Experience in parallel or distributed processing, high performance computing, stream computing.
Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $98,300 - $193,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $127,200 - $208,800 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
Microsoft will accept applications for the role until January 10, 2025.
Responsibilities
- Conduct R&D on intelligent search advertising systems to mine and learn actionable insights from large scale data and signals we collect from user queries and online activities, advertiser created campaigns and their performances, and myriad responses from the parties touched by the system in Bing ads paid search ecosystem.
- Play a key role in driving algorithmic and modeling improvement to the system (esp. using deep learning techniques)
- Analyze performance and identify opportunities based on offline and online testing.
- Develop, and deliver robust and scalable solutions
- Make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.