National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Scientist, Ad Fraud Detection, Traffic Quality ML

Amazon
London
4 weeks ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist - Model Risk Management

Data Scientist- Social Impact

Data Scientist- Social Impact

Data Scientist- Social Impact

Data Scientist- Social Impact

Data Scientist- Social Impact

Data Scientist, Ad Fraud Detection, Traffic Quality ML

Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. One of the key focus areas is Traffic Quality where we endeavour to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace. We do this by building machine learning and optimization algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic. The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion. We are committed to building a long-term traffic quality solution that encompasses all Amazon advertising channels and provides industry leading traffic filtering leveraging GenAI and state-of-the-art deep learning techniques. Our systems preserve advertiser trust and saves them hundreds of millions of dollars of wasted spend.

A Data Scientist is responsible for delivering deep data-driven analyses with insights that drive the business. They would use a combination of analytics, data visualization and machine learning to identify gaps in current solutions as well as prototype new algorithms that close the gaps. The ideal candidate should have strong experience in dive deep analytics and data visualization, thorough knowledge of statistical techniques and strong breadth in machine learning. The candidate should also have good programming and design skills to implement machine learnings algorithms in practice on massive unstructured datasets.


BASIC QUALIFICATIONS

- 3+ years of data scientist experience
- 4+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment

PREFERRED QUALIFICATIONS

- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience with big data: processing, filtering, and presenting large quantities (100K to Millions of rows) of data
- Experience in a ML or data scientist role with a large technology company
- Experience in Computational Advertising

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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.


#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs Skills Radar 2026: Emerging Tools, Frameworks & Platforms to Learn Now

Machine learning is no longer confined to academic research—it's embedded in how UK companies detect fraud, recommend content, automate processes & forecast risk. But with model complexity rising and LLMs transforming workflows, employers are demanding new skills from machine learning professionals. Welcome to the Machine Learning Jobs Skills Radar 2026—your annual guide to the top languages, frameworks, platforms & tools shaping machine learning roles in the UK. Whether you're an aspiring ML engineer or a mid-career data scientist, this radar shows what to learn now to stay job-ready in 2026.

How to Find Hidden Machine Learning Jobs in the UK Using Professional Bodies like BCS, Turing Society & More

Machine learning (ML) continues to transform sectors across the UK—from fintech and retail to healthtech and autonomous systems. But while the demand for ML engineers, researchers, and applied scientists is growing, many of the best opportunities are never posted on traditional job boards. So, where do you find them? The answer lies in professional bodies, academic-industry networks, and tight-knit ML communities. In this guide, we’ll show you how to uncover hidden machine learning jobs in the UK by engaging with groups like the BCS (The Chartered Institute for IT), Turing Society, Alan Turing Institute, and others. We’ll explore how to use member directories, CPD events, SIGs (Special Interest Groups), and community projects to build connections, gain early access to job leads, and raise your professional profile in the ML ecosystem.

How to Get a Better Machine Learning Job After a Lay-Off or Redundancy

Redundancy in machine learning can feel especially frustrating when your role was technically advanced, strategically important, or AI-facing. But the UK still has strong demand for machine learning professionals across fintech, healthtech, retail, cybersecurity, autonomous systems, and generative AI. Whether you're a research-oriented ML engineer, production-focused MLOps developer, or applied scientist, this guide is designed to help you bounce back from redundancy and find a better opportunity that suits your goals.