Data Science Manager, DSP Ranking Science Amazon Jobs – Palo Alto, CA

Amazon Advertising LLC

Amazon jobs in California, United States – Apply for Data Science Manager, DSP Ranking Science Amazon Jobs – Palo Alto, CA. See the job description, requirements, and the link to apply.

Job ID: 2283758

Job Title: Data Science Manager.


Job summary
Amazon Advertising is one of Amazon’s fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!

The Machine Learning Optimization (MLO) team develops algorithms and systems that improve the performance and delivery of Amazon’s Display Advertising campaigns and automates campaign management using machine learning techniques. The team develops and deploys machine learning solutions that drive ad selection, bidding, user response prediction, and automated campaign management. Customers are advertisers and publishers who do business with Amazon.We own the system for batch training of user response prediction models, while the ad serving engineering team owns the real-time model scoring component. This teams owns the system for automated management of advertising campaigns, which can dynamically adjust parameters such as budget, bid prices, and targeting to optimize for campaign performance.

As the Data Science Manager on this team, you will:

  • Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity.
  • Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects.
  • Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.
  • Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization.
  • Analyze historical data to identify trends and support optimal decision making.
  • Apply statistical and machine learning knowledge to specific business problems and data.

-Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.

  • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
  • Build decision-making models and propose effective solutions for the business problems you define.
  • Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.

Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit – with a broad mandate to experiment and innovate.

Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers’ needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.

Team video ~

Key job responsibilities
You will lead a team of scientists working on the next generation of our real-time pricing systems. These systems are optimizing the price of every individual opportunity on behalf of Amazon Advertising advertisers.

A day in the life

  • Set priorities and guide design by mentoring team members
  • Suggest models, designs, features to sophisticate the marketing products or solutions we offer to our customers
  • Coordinate the team initiatives with the larger program development and build the vision

About the team
The Ranking team is responsible for real-time pricing decisions on the Amazon RTB (Real-Time Bidding) system


  • PhD or Master’s Degree in Statistics, Applied Mathematics, Physics, Science, Engineering, Economics, or other quantitative fields.
  • 3+ years of direct people management; managing scientists.
  • 8+ years of experience as a data scientist, economist, applied scientist, research scientist or equivalent data analytics role
  • Expertise in as many of the following: hypothesis testing, estimation, experimental design, hypothesis and A/B testing, causal inferencing, multi-variate testing & design, descriptive analytics, and regression analysis.
  • Experience with data scripting languages (e.g. SQL, Python, R) or statistical/mathematical software (e.g. R, SAS, or Matlab).
  • Familiarity with probability, probability distributions, statistics and causal inference.
  • Good understanding of apply machine learning to solve real-world problems.




  • Broad knowledge of ML methods, statistical analysis, and problem-solving skills.
  • Expert level knowledge in statistics; sophisticated user of statistical tools.
  • Experience processing, filtering, and presenting large quantities (hundreds of millions/billions of rows) of data
  • Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization.
  • Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
  • Excellent verbal and written communication skills with the ability to advocate technical solutions for science, engineering, and business audiences.
  • Ability to develop experimental and analytical plans for data modeling, use effective baselines, and accurately determine cause-and-effect relations.
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