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Senior Applied Scientist, Ads Measurement Science

Amazon

Senior Applied Scientist, Ads Measurement Science

Amazon

New York, NY, USA

·

On-site

·

Full-time

·

1mo ago

Compensation

$183,800 - $248,700

Benefits & Perks

Competitive salary and equity package

Comprehensive health, dental, and vision insurance

Flexible work arrangements

Professional development budget

Equity

Healthcare

Flexible Hours

Learning

Required Skills

Node.js

TypeScript

Python

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions that measure the comprehensive impact of ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific methods, approaches and technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision.
As a Senior Applied Scientist on the team, you will lead the development of measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers.

  • Key job responsibilities
  • Lead a team of scientists to innovate on state-of-the-art ads measurement solutions leveraging artificial intelligence, causal inference, natural language processing, computer vision, and large language models.
  • Directly contribute to the end-to-end delivery of production solutions through careful designs and owning implementation of significant portions of critical-path code
  • Lead the decomposition of problems and development of roadmaps to execute on it.
  • Set an example for others with exemplary analyses; maintainable, extensible code; and simple, effective solutions.
  • Influence team business and engineering strategies.
  • Communicate clearly and effectively with stakeholders to drive alignment and build consensus on key initiatives.
  • Foster collaborations among scientists and engineers to move fast and broaden impact.
  • Actively engage in the development of others, both within and outside of the team.
  • Regularly engage with the broader science community with presentations, publications, and patents.
    About the team
    We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

Basic Qualifications

  • 5+ years of building machine learning models for business application experience
  • PhD, or Master's degree and 8+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning

Preferred Qualifications

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, Mx Net, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems such as Hadoop, Spark etc.
  • Experience with popular deep learning frameworks such as Mx Net and Tensor Flow.
  • 5+ years of building machine learning models or developing algorithms for business application experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
    Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
    Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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, NY, New York - 183,800.00 - 248,700.00 USD annually

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About Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

Employees

Seattle

Headquarters

Reviews

2.9

10 reviews

Work Life Balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

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Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

2 data points

Junior/L3

L2

L3

L4

L5

L6

M3

M4

M5

M6

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L4

0 reports

$181,968

total / year

Base

-

Stock

-

Bonus

-

$154,672

$209,264

Interview Experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge