Principal Applied Scientist - Auction & Bidding | Microsoft Careers

Microsoft

Hybrid Actively hiring
US Posted 33 days ago $139,900$274,800 / year

At a glance

AI generated

TL;DR

The Applied Scientist role at Microsoft Advertising's Ads Monetization Algo team involves optimizing ad performance through real-time bidding strategies and auction mechanism design. This senior-level position requires expertise in quantitative fields such as machine learning, optimization theory, and game theory to develop robust algorithms that enhance advertiser ROI and the overall health of the marketplace. Day-to-day responsibilities include designing bidding strategies, prototyping auction mechanisms, conducting large-scale experiments, and implementing automation algorithms using AI and ML techniques. The ideal candidate will have extensive experience in developing live production systems and a background in auto-bidding or auction design, contributing to Microsoft Advertising's annual revenue of over 10 billion dollars through scalable solutions that impact millions of users globally.

Skills

Python Java C++ SQL Kubernetes Docker AWS Azure CI/CD TensorFlow PyTorch Scikit-learn Pandas Numpy Spark R Jupyter Git GitHub PostgreSQL MSSQL Hadoop A/B Testing Machine Learning Optimization Theory Control Theory Game Theory Economics Operations Research Causal Inference Information Retrieval

What you'll do

  • Design bidding strategies using optimization and control theory techniques.
  • Analyze and prototype auction mechanisms for specific product areas.
  • Oversee large-scale experiments to enhance marketplace health with advanced analytics.
  • Develop automation algorithms for advertisers to boost ROI through AI and ML.
  • Implement methods to optimize the ad marketplace at a large scale efficiently.

What we're looking for

  • Extensive experience in statistical machine learning and decision theory.
  • Strong background in optimization theory, mathematical modeling, and data mining.
  • Experience designing and analyzing auction mechanisms using game theory and mechanism design.
  • Proficient in developing automation algorithms for advertisers to enhance ROI.
  • Expertise in implementing large-scale systems for marketplace optimization.

Market check

Salary context

This $139,900–$274,800 range sits above 64% of similar postings on FindRole.

Peer median band

$139,900$238,600

Median floor and ceiling across peers.

Typical midpoint (25–75%)

$162,000$214,500

Middle half of comparable postings.

Based on 240 comparable postings.

* 240 is the maximum number of comparable postings sampled.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 534 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 488 roles with salary data.

Most-posted roles

View all roles at Microsoft

More like this

Similar roles

Principal Applied Scientist | Microsoft Careers

Microsoft

US 3 days ago $142,800$274,800
Python TensorFlow PyTorch Keras Scikit-learn Pandas NumPy AWS Azure Google Cloud Platform Docker Kubernetes CI/CD Git Jupyter Notebook SQL PostgreSQL Redis Hadoop Spark A/B Testing Reinforcement Learning Generative AI Transformers LLM
Hybrid

| Microsoft Careers

Microsoft

US 18 days ago $119,800$234,700
Python Java C++ Scala R TensorFlow PyTorch Keras Scikit-learn Spark MLOps Docker Kubernetes AWS Azure CI/CD A/B Testing PostgreSQL Redis Hadoop Bigtable LLMs GenerativeAI ReinforcementLearning Transformers
Hybrid

Principal Applied Scientist | Microsoft Careers

Microsoft

Redmond, WA 60 days ago $139,900$274,800
Python Java C++ Spark Hadoop Cosmos Azure Kubernetes Docker CI/CD TensorFlow PyTorch PostgreSQL MongoDB Elasticsearch Git Jenkins
Hybrid

Principal Applied Scientist | Microsoft Careers

Microsoft

US 5 days ago $142,800$274,800
PyTorch TensorFlow LLM-based ranking agentic AI generative AI recommendation systems deep learning modern ML frameworks cloud-scale infrastructure distributed pipelines high-throughput online services data processing systems multi-tiered distributed services large-scale data analytics Spark asynchronous programming multi-objective optimization CI/CD
Hybrid