Principal Applied Scientist, Ads Optimization, FAIM

Amazon.com, Inc.
Seattle, WA, United States
5 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$228,700.0 - $309,400.0
Working hours
Regular working hours

Tech stack

Java (Programming Language) Algorithm Design C++ (Programming Language) Python (Programming Language) Machine Learning Big O Information Technology

Job description

As a Principal Applied Scientist for Full-Funnel Campaign optimization, you will invent the models that jointly allocate budget across sponsored ad products to maximize advertiser outcomes and long-term customer value.

This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth.

  • Own the science vision for full-funnel campaign optimization end to end
  • Invent models for joint budget allocation, incrementality, and long-term value
  • Take innovations from prototype to production serving live advertiser campaigns
  • Raise the technical bar across scientists and engineers, Define the long-term scientific vision for full-funnel campaign optimization, translating ambiguous advertiser needs and competing objectives into a concrete science roadmap.
  • Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase.
  • Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value.
  • Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction.
  • Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production.
  • Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration.
  • Grow scientific talent and publish impactful research internally and at top-tier venues.

A day in the life You move between deep technical work and org-wide influence. A morning might be spent deriving a budget-allocation formulation with two scientists, then reviewing an incrementality experiment design over an advertiser segment. Afternoons bring roadmap alignment with product and engineering partners, a design review that raises the bar on a teammate’s model, and a working session on taking a prototype to production. Your customers are Amazon advertisers and their shoppers; your stakeholders span applied science, engineering, and product leadership across the Ads full-funnel organization.

About the team We build the optimization science behind full-funnel advertising on Amazon: how campaigns are recommended, and how budget is allocated across sponsored ad products to grow advertiser outcomes and long-term customer value. Our mission is to make full-funnel advertising work automatically and measurably for every advertiser, from foundational research through production systems serving live campaigns. We are a science-driven, high-ownership team that values rigorous experimentation, invention where no proven approach exists yet, and close partnership with engineering and product.

Requirements

PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field

  • 5+ years of hands-on experience in predictive modeling and analysis
  • Experience distilling informal customer requirements into problem definitions while dealing with ambiguity and competing objectives
  • Experience programming in Java, C++, Python, or related language
  • Experience leading experienced scientists, as well as a record of developing junior members from academia or industry into a career track in a business environment, 10+ years of relevant experience in industry or academia
  • Knowledge of problem solving, algorithm design, and complexity analysis
  • Experience creating novel algorithms and advancing the state of the art
  • Peer-reviewed scientific contributions in premier journals and conferences

Benefits & conditions

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Palo Alto - 228,700.00 - 309,400.00 USD annually USA, WA, SEATTLE - 198,900.00 - 269,000.00 USD annually

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.amazon.jobs

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:36 min

Applying supervised machine learning for practical rule extraction

Katja TrƤumner

2:13 min

Embedding fairness into algorithmic design and continuous evaluation pipelines

Prathyusha Charagondla Ā· LIVE

3:01 min

Evaluating generated sorting algorithms and application memory complexity

Markus Walker Markus Walker Ā· WWC 2023

2:37 min

Optimizing technical profiles for AI sourcing and recruitment

Mina Golesorkhi Mina Golesorkhi Ā· WWC Europe 2026

1:57 min

Evolution of machine learning algorithms and computing hardware

Alexandra Waldherr Ā· LIVE

3:51 min

Technical skills and collaborative mindsets for mobility engineering roles

Georg Kühberger +1 · LIVE

Videos

See all

Related articles

See all