Senior Applied Scientist, Measurement, Ad Tech, and DataScience (MADS)

Senior Applied Scientist, Measurement, Ad Tech, and

Data Science (MADS)

The Measurement, Ad Tech,

and Data Science (MADS) team at Amazon Ads is at the forefront of

developing innovative solutions that help tens of millions of

advertisers understand the value of their ad spend while

prioritizing customer privacy and measurement quality. The Media

Planning Science team, part of the broader MADS team, develops and

implements models that deliver insights and recommendations for

strategic media planning and measurement across Amazon

Advertising's product portfolio. Our mission is to help advertisers

create and execute plans that meet their objectives while providing

accurate measurement tools. We work on a multitude of problem

statements that encompass Reach and Frequency, Budget Planning

Optimization, and Recommendations. Our models leverage both

heuristic and machine learning approaches including deep learning

techniques, with insights delivered through agent-based tools and

APIs that integrate seamlessly into user interfaces and

programmatic systems to ensure optimal advertising outcomes. As a

Senior Applied Scientist on the team, you will be at the forefront

of innovation, developing media planning solutions end‑to‑end from

inception to production. You will set the technical vision and

innovate on behalf of our customers. You will propose, design,

analyze, and productionize models to provide novel measurement

insights to our customers. You will partner with engineering to

deploy these solutions into production. You will work with key

stakeholders from various business teams to enable advertisers to

act upon those metrics. Key Job

Responsibilities

Lead the development of media

planning models and solutions that address the full spectrum of an

advertiser’s investment, focusing on scalable and efficient

methodologies. Collaborate closely with cross‑functional teams

including engineering, product management, and business teams to

define and implement measurement solutions. Use state‑of‑the‑art

scientific technologies including Generative AI, Classical Machine

Learning, Causal Inference, Natural Language Processing, and

Computer Vision to develop state‑of‑the‑art models that measure the

impact of media plans across different metrics. Drive

experimentation and the continuous improvement of ML models through

iterative development, testing, and optimization. Translate complex

scientific challenges into clear and impactful solutions for

business stakeholders. Mentor and guide junior scientists,

fostering a collaborative and high‑performing team culture. Foster

collaborations between scientists to move faster, with broader

impact. Regularly engage with the broader scientific community with

presentations, publications, and patents. A Day in the

Life

You will solve real‑world problems by

analyzing large amounts of data, generating business insights and

opportunities, designing simulations and experiments, and

developing ML / DL models. The team is driven by business needs,

which requires collaboration with other Scientists, Engineers, and

Product Managers across the advertising organization. You will

prepare written and verbal documents to share insights to audiences

of varying levels of technical sophistication. About the

Team

We are a team of scientists across

Applied, Research, and Data Science disciplines. You will work with

colleagues with deep expertise in ML, DL, NLP, 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

3+ years of building machine

learning models for business application experience PhD, or Masters

degree and 6+ 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, MxNet, Tensorflow, numpy,

scipy etc. Experience with large scale distributed systems such as

Hadoop, Spark etc. PhD in engineering, technology, computer

science, machine learning, robotics, operations research,

statistics, mathematics or equivalent quantitative field Amazon is

an equal opportunity employer and does not discriminate on the

basis of protected veteran status, disability, or other legally

protected status. The base salary range for this position is

195,900.00 - 327,200.00 CAD annually. Amazon offers comprehensive

benefits including health insurance (medical, dental, vision,

prescription, basic life & AD&D insurance), Registered

Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan

DPSP), paid time off, and other resources to improve health and

well‑being.

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