Anton van den Hengel is seen smiling into the camera, with some office buildings in the background
Anton van den Hengel

Anton van den Hengel’s journey from intellectual property law to computer vision pioneer

Amazon’s director of applied science in Adelaide, Australia, believes the economic value of computer vision has “gone through the roof".

Anton van den Hengel, an international pioneer in computer vision and its many applications, departed the University of Adelaide in South Australia to join Amazon as director of applied science in April 2020. He is creating a new, world-class machine-learning hub in Adelaide and supporting Amazon’s business through the development and application of state-of-the-art computer vision and scalable machine learning.

Related content
Senior principal scientist Aleix M. Martinez on why computer vision research has only begun to scratch the surface.

In 2018, van den Hengel was the founding director of the Australian Institute for Machine Learning (AIML), Australia’s first institute dedicated to machine learning research. When he left to join Amazon, AIML was 140 people strong and near the top of the institutional world rankings in terms of computer vision research. He remains the part-time director of AIML’s new Centre for Augmented Reasoning, whose mission is to build core Artificial Intelligence (AI) capability in Australia.

Van den Hengel has authored more than 300 research papers, commercialized eight patents, and been chief investigator on research projects funded by many Fortune 500 companies.

But it could all have been so different. The young van den Hengel first got into computer science simply to support his efforts to become an intellectual property lawyer. In fact, he completed his law degree.

Amazon in Australia
Research teams in Adelaide are developing state-of-the-art, large-scale machine learning methods and applications involving terabytes of data. They work on applying ML, and particularly computer vision, to a wide spectrum of areas.

“I’d bought the suit, tie, and bright white shirt and was all ready to start my first day as an entry level lawyer,” he recalls. “Then, instead, I turned around and went straight back into the University of Adelaide. I spent the next couple of decades there.”

What followed was a master’s, then PhD in computer science and, ultimately, building up the University of Adelaide’s forerunner to AIML, the Australian Centre for Visual Technologies.

The chance to have an impact

What turned van den Hengel around was the chance to study computer vision.

“I saw the opportunity to engage with something that I realized was going to have incredible impact,” he says. Computer vision and its applications are everywhere today, but in the early 1990s, things were very different. “It's hard to believe now but at the time there were maybe 1000 people in the world working on computer vision, at a time when there weren't any digital cameras,” he reminisces. “Most papers in CV were at least half about how people had taken the images.”

[In the early 90s] there were maybe 1000 people in the world working on computer vision, at a time when there weren't any digital cameras. Most papers in CV were at least half about how people had taken the images.
Anton van den Hengel

Van den Hengel understood that humans are primarily visual animals and he clearly saw the inevitability of computers using vision to sense, and ultimately interact with, the world. “But back then, having a computer that could actually either measure or impact upon the real world was virtually unbelievable,” he says.

Since then, he says, computer vision has transformed from a heavily mathematical field with 300 people at every conference who all knew each other, to conferences of many thousands of people and auditoria full of companies trying to attract staff and sell things.

“The economic value of computer vision has gone through the roof,” he says.

Computer vision is a fundamental technology, van den Hengel says, because it relates the real world to symbols. “Humans reason about things in terms of symbols, so ‘cat’, ‘sky’, ‘car’, ‘road’, and ‘fish’ are all symbols, right? Computer vision takes visual signals from the real world and relates those signals to symbols,” he says.

That's been the critical missing piece of the puzzle. For decades it was predicted that by the year 2000 we would have robots doing the housework and many other ‘magical’ things, but we came up short because there's an infinite variation of things out there in the real world and it's much harder to get a computer to reason about our physical environment than anybody imagined.”

Looking for answers

This missing piece is tackled by a subfield of computer vision known as visual question answering (VQA). The idea is to enable computers not only to understand the content of an image (or video/livestream) in a more semantic, human-like way, but also to answer questions posed in natural language about that image. For example, “Where was this photo taken?”, “Does it look like the person on the picnic blanket is expecting someone?”, “What’s the color of the dog nearest the stop sign?”.

Van den Hengel is the world’s most-cited researcher in VQA by an enormous margin, with close to 22,000 citations.

Fireside chat: Anton van den Hengel and Simon Lucey

“I got into it very early because I saw it as a threshold change in the way that artificial intelligence works,” van den Hengel says. “What's interesting about VQA is that you ask the question at run-time and need the answer immediately, so it needs to be very flexible, unlike current machine learning applications, which are often fixed, single-purpose solutions to specific problems.”

In other words, it needs to be closer to true artificial intelligence – often referred to as artificial general intelligence.

In that vein, imagine a robot that could follow natural-language instructions, based on a greater understanding of what it sees around itself. It’s a sci-fi dream, but for how much longer?

In 2018, using a vision-and-language process similar to VQA, Van den Hengel and a team of colleagues from across Australia developed a simulator that uses imagery taken from the inside of real buildings to teach virtual agents to successfully navigate using visually grounded instructions, such as: “Head upstairs and walk past the piano through an archway directly in front. Turn right when the hallway ends at pictures and table. Wait by the moose antlers hanging on the wall.” It is only a matter of time before we can talk to our self-driving cars in a similar manner when necessary, says van den Hengel.

The power of neural networks

Rapid developments in machine learning are behind the recent supercharging of computer vision research.

“In the last 10 years of computer vision, we have essentially trained deep-learning neural networks to replace all of these lovely computer-vision algorithms that we'd previously come up with for solving a whole bunch of problems,” he says. “In fact, neural networks are so much better at it, they went from being just an interesting solution to a puzzle to being a practical solution to some of the core challenges we face.”

While at the University of Adelaide, van den Hengel has applied advances in ML and computer vision to make the world better in a variety of ways. These include working with Adelaide-based medical technology company LBT Innovations in creating an automated pathology machine called APAS (Automated Plate Assessment System) Independence, which can screen and interpret high volumes of pathology plates.

“There's a shortage of trained pathologists, partly because it's not a lot of fun sitting all day doing chemistry and looking at samples. APAS does the drudge work of the visual inspection process,” he says. The device was FDA approved in 2019.

Beyond computer vision, van den Hengel is currently the chief investigator for the Australian National Health and Medical Research Council’s Centre of Research Excellence in Healthy Housing, which is using ML to help deliver better outcomes within the Australian housing system, not only in terms of housing, but also in terms of health.

“People who are homeless suffer diseases and injuries, which put them into hospital, and homelessness can see people spiral into a set of difficult conditions that are very expensive for society to address,” he says. “It's actually cheaper to house somebody than to fix the impact of homelessness. So where can we intervene in the housing process in a way that benefits everybody and also saves money?”

Not all of van den Hengel’s work is quite so serious, however.

The paper I'm most happy about but that gets the least recognition is one that tells you how to build real Lego models of objects in images,” he says. “It’s got brilliant maths in it; some of my favorite maths. And it incorporates gravity, structural considerations and, you know, fantastic maths.” And did he mention the maths?

Van den Hengel has even used ML to design an IPA beer.

“Collecting the data was a real trauma: we had to drink, and rate, a lot of beer,” he laments. He named the resulting ale The Rodney, in homage to the Australian AI researcher and roboticist Rodney Brooks, whose work resulted in the Roomba vacuum cleaner.

Joining Amazon

Always an advocate for Australia on the world stage, van den Hengel was keen to play a leading role in Amazon’s research push into the country. “It was a fantastic opportunity to start a new group in Australia for a company like Amazon.”

Typically, when academics transition to Amazon, they talk about the increase in pace from academia to industry. Van den Hengel bucks that trend.

“I was running a group with 140 people, trying to make enough money to pay them, keep the doors open, deliver on projects for tens of millions of dollars, doing PR, you name it,” he says. “Here, I've got about 25 world-class people with PhDs who work for me and 12 interns.”

Van den Hengel noted that Amazon is a results-focused environment. “At Amazon you are expected to deliver, but you do it with an engineering team and support systems all geared towards delivering customer benefit.”

So what is van den Hengel delivering on? A current project is applying visual inspection methods to help to make sure that Amazon customers get the best fresh produce possible.

I think the whole retail field is moving towards a better understanding of the nature of objects in the world and how humans relate to those objects, or products. And that's something that computer vision is particularly well-placed to deliver.
Anton van den Hengel

“Visual inspection is a magnificent challenge and a core problem in computer vision,” he says,” and addressing it means we can make sure that when a customer receives a delivery of, say, tomatoes, they are as perfect as can be.”

Another key project involves using computer vision and ML to understand in a deeper way the hundreds of millions of items in the ever-changing Amazon catalogue. The catalogue has a trove of information, both in the word-based product descriptions and the images supplied by sellers.

“Making the most of the information contained in these two sources of information – which is essentially what humans do – is an interesting challenge, because it relies on the relationships between visual signals and symbols,” he explains, adding that cracking this challenge will help customers who are using Amazon search find the product that best matches their need “even if they're not entirely sure how best to specify it themselves.”

Despite the considerable demands of managing a growing team, van den Hengel is determined to remain hands-on with his own research. “Amazon's an innovative company, and really, truly innovating in a way that's going to provide something of value to customers that nobody else can means that you need managers who deeply understand where the technology can go,” he says.

So where is the technology going?

“I think the whole retail field is moving towards a better understanding of the nature of objects in the world and how humans relate to those objects, or products,” he says. “And that's something that computer vision is particularly well-placed to deliver.”

Browse through the open science positions in Amazon's Australia offices.

Research areas

Related content

US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We are seeking a highly skilled weather scientist to help invent and develop new models and strategies to support Prime Air’s drone delivery program. In this role, you will develop, build, and implement novel weather solutions using your expertise in atmospheric science, data science, and software development. You will be supported by a team of world class software engineers, systems engineers, and other scientists. Your work will drive cross-functional decision-making through your excellent oral and written communication skills, define system architecture and requirements, enable the scaling of Prime Air’s operation, and produce innovative technological breakthroughs that unlock opportunities to meet our customers' evolving demands. About the team Prime air has ambitious goals to offer its service to an increasing number of customers. Enabling a lot of concurrent flights over many different locations is central to reaching more customers. To this end, the weather team is building algorithms, tools and services for the safe and efficient operation of prime air's autonomous drone fleet.
US, CA, Palo Alto
Amazon Sponsored Products is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of GenAI/LLM powered self-service performance advertising products that drive discovery and sales. Our products are strategically important to Amazon’s Selling Partners and key to driving their long-term growth. We deliver billions of ad impressions and clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. This role will be pivotal within the Autonomous Campaigns org of Sponsored Products Ads, where we're pioneering the development of AI-powered advertising innovations that will redefine the future of campaign management and optimization. As a Principal Applied Scientist, you will lead the charge in creating the next generation of self-operating, GenAI-driven advertising systems that will set a new standard for the industry. Our team is at the forefront of designing and implementing these transformative technologies, which will leverage advanced Large Language Models (LLMs) and sophisticated chain-of-thought reasoning to achieve true advertising autonomy. Your work will bring to life systems capable of deeply understanding the nuanced context of each product, market trends, and consumer behavior, making intelligent, real-time decisions that surpass human capabilities. By harnessing the power of these future-state GenAI systems, we will develop advertising solutions capable of autonomously selecting optimal keywords, dynamically adjusting bids based on complex market conditions, and optimizing product targeting across various Amazon platforms. Crucially, these systems will continuously analyze performance metrics and implement strategic pivots, all without requiring manual intervention from advertisers, allowing them to focus on their core business while our AI works tirelessly on their behalf. This is not simply about automating existing processes; your work will redefine what's possible in advertising. Our GenAI systems will employ multi-step reasoning, considering a vast array of factors, from seasonality and competitive landscape to macroeconomic trends, to make decisions that far exceed human speed and effectiveness. This autonomous, context-aware approach represents a paradigm shift in how advertising campaigns are conceived, executed, and optimized. As a Principal Applied Scientist, you will be at the forefront of this transformation, tackling complex challenges in natural language processing, reinforcement learning, and causal inference. Your pioneering efforts will directly shape the future of e-commerce advertising, with the potential to influence marketplace dynamics on a global scale. This is an unparalleled opportunity to push the boundaries of what's achievable in AI-driven advertising and leave an indelible mark on the industry. Key job responsibilities • Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business using GenAI, LLM, and ML solutions. • Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in AI/ML. • Design and lead organization-wide AI/ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our advertisers. • Work with our engineering partners and draw upon your experience to meet latency and other system constraints. • Identify untapped, high-risk technical and scientific directions, and devise new research directions that you will drive to completion and deliver. • Be responsible for communicating our Generative AI/ Traditional AI/ML innovations to the broader internal & external scientific community.
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As a Data Scientist on this team you will: - Lead Data Science solutions from beginning to end. - Deliver with independence on challenging large-scale problems with complexity and ambiguity. - Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data. - Build Machine Learning and statistical models to solve specific business problems. - Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, CO, Boulder
Do you want to lead the Ads industry and redefine how we measure the effectiveness of the WW Amazon Ads business? Are you passionate about causal inference, Deep Learning/DNN, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be an Applied Science leader within our Advertising Incrementality Measurement science team! Key job responsibilities As an Applied Science leader within the Advertising Incrementality Measurement (AIM) science team, you are responsible for defining and executing on key workstreams within our overall causal measurement science vision. In particular, you will lead the science development of our Deep Neural Net (DNN) ML model, a foundational ML model to understand the impact of individual ad touchpoints for billions of daily ad touchpoints. You will work on a team of Applied Scientists, Economists, and Data Scientists to work backwards from customer needs and translate product ideas into concrete science deliverables. You will be a thought leader for inventing scalable causal measurement solutions that support highly accurate and actionable causal insights--from defining and executing hundreds of thousands of RCTs, to developing an exciting science R&D agenda. You will solve hard problems, advance science at Amazon, and be a leading innovator in the causal measurement of advertising effectiveness. In this role, you will work with a team of applied scientists, economists, engineers, product managers, and UX designers to define and build the future of advertising causal measurement. You will be working with massive data, a dedicated engineering team, and industry-leading partner scientists. Your team’s work will help shape the future of Amazon Advertising.