Machine Learning Engineer / Economist, Ads Marketplace
Machine Learning Engineer / Economist, Ads Marketplace
Job Description
Responsibilities
- Build statistical models and production systems to improve marketplace design and operations for Pinners, Partners, and Pinterest.
- Tune marketplace parameters (e.g., utility function), optimize ad diversity and load, implement auctions, and model long-term effects to reduce ad fatigue and improve advertiser outcomes.
- Define and implement experiments to understand long term Marketplace effects.
- Develop strategies to balance long and short term business objectives.
- Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.
- Application areas: marketplace performance analysis, advertiser churn/retention modeling, promotional bandwidth allocation, ranking/pricing/mechanism design, bidding/budgeting innovation, and anticipating second-order effects for new ad offerings.
Qualifications
- Degree in Computer Science, Machine Learning, Economics, Operations Research, Statistics or a related field.
- Industry experience in applying economics or machine learning to real products (e.g., ads auctions, pricing, marketplaces, or large-scale recommendation/search systems).
- Knowledge in auction theory, market design, and econometrics with excellent data analysis skills.
- Strong software engineering and mathematical skills and proficient with statistical methods.
- Experience with online experimentation and causal inference (A/B testing, long-running experiments, or similar) in large-scale systems.
- Practical understanding of machine learning algorithms and techniques.
- Impact-driven, highly collaborative, and an effective communicator
Preferred Qualifications
- Prior ads or two-sided marketplace experience
Locations
- San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
Salary
189,721 - 332,012 USD / yearly
Skills Required
- Auction theoryintermediate
- Market designintermediate
- Econometricsintermediate
- Data analysisintermediate
- Software engineeringintermediate
- Mathematical skillsintermediate
- Statistical methodsintermediate
- Online experimentationintermediate
- Causal inferenceintermediate
- A/B testingintermediate
- Machine learning algorithms and techniquesintermediate
Required Qualifications
- Degree in Computer Science, Machine Learning, Economics, Operations Research, Statistics or a related field. (experience)
- Industry experience in applying economics or machine learning to real products (e.g., ads auctions, pricing, marketplaces, or large-scale recommendation/search systems). (experience)
- Knowledge in auction theory, market design, and econometrics with excellent data analysis skills. (experience)
- Strong software engineering and mathematical skills and proficient with statistical methods. (experience)
- Experience with online experimentation and causal inference (A/B testing, long-running experiments, or similar) in large-scale systems. (experience)
- Practical understanding of machine learning algorithms and techniques. (experience)
- Impact-driven, highly collaborative, and an effective communicator (experience)
Preferred Qualifications
- Prior ads or two-sided marketplace experience (experience)
Responsibilities
- Build statistical models and production systems to improve marketplace design and operations for Pinners, Partners, and Pinterest.
- Tune marketplace parameters (e.g., utility function), optimize ad diversity and load, implement auctions, and model long-term effects to reduce ad fatigue and improve advertiser outcomes.
- Define and implement experiments to understand long term Marketplace effects.
- Develop strategies to balance long and short term business objectives.
- Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.
- Application areas: marketplace performance analysis, advertiser churn/retention modeling, promotional bandwidth allocation, ranking/pricing/mechanism design, bidding/budgeting innovation, and anticipating second-order effects for new ad offerings.
Tags & Categories
Machine Learning Engineer / Economist, Ads Marketplace
Machine Learning Engineer / Economist, Ads Marketplace
Job Description
Responsibilities
- Build statistical models and production systems to improve marketplace design and operations for Pinners, Partners, and Pinterest.
- Tune marketplace parameters (e.g., utility function), optimize ad diversity and load, implement auctions, and model long-term effects to reduce ad fatigue and improve advertiser outcomes.
- Define and implement experiments to understand long term Marketplace effects.
- Develop strategies to balance long and short term business objectives.
- Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.
- Application areas: marketplace performance analysis, advertiser churn/retention modeling, promotional bandwidth allocation, ranking/pricing/mechanism design, bidding/budgeting innovation, and anticipating second-order effects for new ad offerings.
Qualifications
- Degree in Computer Science, Machine Learning, Economics, Operations Research, Statistics or a related field.
- Industry experience in applying economics or machine learning to real products (e.g., ads auctions, pricing, marketplaces, or large-scale recommendation/search systems).
- Knowledge in auction theory, market design, and econometrics with excellent data analysis skills.
- Strong software engineering and mathematical skills and proficient with statistical methods.
- Experience with online experimentation and causal inference (A/B testing, long-running experiments, or similar) in large-scale systems.
- Practical understanding of machine learning algorithms and techniques.
- Impact-driven, highly collaborative, and an effective communicator
Preferred Qualifications
- Prior ads or two-sided marketplace experience
Locations
- San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
Salary
189,721 - 332,012 USD / yearly
Skills Required
- Auction theoryintermediate
- Market designintermediate
- Econometricsintermediate
- Data analysisintermediate
- Software engineeringintermediate
- Mathematical skillsintermediate
- Statistical methodsintermediate
- Online experimentationintermediate
- Causal inferenceintermediate
- A/B testingintermediate
- Machine learning algorithms and techniquesintermediate
Required Qualifications
- Degree in Computer Science, Machine Learning, Economics, Operations Research, Statistics or a related field. (experience)
- Industry experience in applying economics or machine learning to real products (e.g., ads auctions, pricing, marketplaces, or large-scale recommendation/search systems). (experience)
- Knowledge in auction theory, market design, and econometrics with excellent data analysis skills. (experience)
- Strong software engineering and mathematical skills and proficient with statistical methods. (experience)
- Experience with online experimentation and causal inference (A/B testing, long-running experiments, or similar) in large-scale systems. (experience)
- Practical understanding of machine learning algorithms and techniques. (experience)
- Impact-driven, highly collaborative, and an effective communicator (experience)
Preferred Qualifications
- Prior ads or two-sided marketplace experience (experience)
Responsibilities
- Build statistical models and production systems to improve marketplace design and operations for Pinners, Partners, and Pinterest.
- Tune marketplace parameters (e.g., utility function), optimize ad diversity and load, implement auctions, and model long-term effects to reduce ad fatigue and improve advertiser outcomes.
- Define and implement experiments to understand long term Marketplace effects.
- Develop strategies to balance long and short term business objectives.
- Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.
- Application areas: marketplace performance analysis, advertiser churn/retention modeling, promotional bandwidth allocation, ranking/pricing/mechanism design, bidding/budgeting innovation, and anticipating second-order effects for new ad offerings.
Tags & Categories