Applied Scientist 2

Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion 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. The rapid growth of online advertising has created enormous opportunities as well as 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. 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 understanding, document/ad understanding, user targeting
- User response (click & conversion) prediction using large scale machine learning algorithms
- Ads Trust & Safety : Ads Editorial, Network protection, fraud detection, traffic quality measurement
- Marketplace mechanism design and optimization, and whole-page experience optimization
- Personalization
- Innovative new ads products
- 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.

Large scale machine learning algorithms and platform

We heavily use the recent advances in grid or cloud computing infrastructure to harness huge volume of data for solving many of the above-mentioned problems. We love big data The RnR 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, and directly impact our business. Our experimentation infrastructure allows us to innovate and test new algorithms rapidly with live traffic to measure their effectiveness, and launch them in production as soon as they produce positive results, which makes our work environment productive and rewarding.

**Responsibilities**:
**We are looking for **A**pplied **S**cientist with deep R&D background to conduct research and development on intelligent search advertising system 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 **Microsoft** ads paid search ecosystem. The person will play a key role to drive algorithmic and modeling improvement to the system, 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.**

**Qualifications**:

- 4+ software development experience in one of the major programming languages: C++, C#, C, or Python required.
- 2+ years hands-on experience with PyTorch, Onnx, or Tensorflow.
- Strong knowledge and skills in machine learning software development and architectures for machine learning (with focus on deep learning).
- Research or Industry experience with one or more of following areas natural language processing, information retrieval, computer vision, large scale machine learning, reinforcement learning.
- Bachelor degree in computer science or a related field is required.
- Excellent problem solving and data analysis skills.
- Passionate, self-motivated.
- Effective communication skills, both verbal and written.
- Strong software design and development skills/experience.

**Preferred Qualifications**:

- Phd/MS in a machine-learning related area with publications in top tier conferences
- Experience in building, deploying, and improving large scale Machine Learning models and algorithms in real-world products.