Amazon Glamazon Gay Pride Month LGBTQIA+ Black Lives Matter
From top left to bottom right: Luyolo Magangane, applied scientist; Ruiwei Jiang, research scientist; Sheeraz Ahmad, applied scientist; Liz Dugan, user experience researcher; Shane McGarry, data scientist; Abhinav Aggarwal, applied scientist.
Credit: Glynis Condon

Pride and prejudice: 6 Amazon scientists share their experiences

Scientists from glamazon, Amazon’s LGBTQIA+ affinity group, say this year's Pride Month is as much about solidarity as it is about celebration.

In most cities around the world June is considered Pride Month, where people celebrate diversity and inclusion. It usually culminates in a parade or march to promote the self-affirmation, equality, and visibility of the lesbian, gay, bisexual, transgender, queer or questioning, intersex, and asexual or allied (LGBTQIA+) community.

At Amazon, it's no different. There's a community of more than 7,000 employees from across the globe who are part of glamazon, an affinity group and employee network, whose mission is to connect those interested in LGBTQIA+ issues to company resources and to each other and to showcase Amazon’s acceptance in communities worldwide.

Given current events, particularly global protests resulting from the videotaped killing of George Floyd by law enforcement officials and the recent U.S. Supreme Court ruling upholding LGBTQIA+ equality, we asked some of the scientists within this affinity group about the significance of this year’s Pride Month.

Abhinav Aggarwal, applied scientist, Alexa Trust

Abhinav Aggarwal (pronouns: he/him/they/them) joined Amazon about nine months ago, after obtaining his PhD in computer science from the University of New Mexico in 2019. His work focuses on building customer trust by designing privacy-preserving machine learning algorithms for handling customer data.

Abhinav Aggarwal, applied scientist, Alexa Trust
Abhinav Aggarwal, applied scientist, Alexa Trust

“Since I joined Amazon, I’ve only had a very passive interaction with glamazon through emails. But I feel like the variety of topics discussed there is absolutely amazing. It’s not just LGBTQIA+ issues; there are thoughts about body positivity, gender pronouns, having pronouns on badges, and issues around diversity and inclusion,” he said.

“But I’d like to see more gender-neutral restrooms in the buildings and use of the ‘they’ pronoun by default,” he says. “Whenever I refer to someone I don’t personally know or even know of at all, I default to using ‘they/them’ as a pronoun. It would be nice to see this as common practice and not assuming someone’s gender based on familiarity with the name, which aligns with the removal of unconscious bias and helps with acceptance.”

With privacy and fairness in AI becoming an increasingly important topic, Aggarwal sees similar issues within his field.

“You don’t want your models for services like Alexa to give you results that are gender-biased, especially as we move towards a more gender-neutral world,” Aggarwal explains. “Ideally, our models should produce gender-agnostic results, and we must work backwards from this goal when defining gender-based fairness. That’s something I’ve felt a lot of pushback with within the industry, because the problem becomes far more complex if you talk about gender neutrality and the continuous spectrum of gender, instead of just the binary male or female.”

Aggarwal sees celebrating Pride Month as a step towards this awareness.

“I think these movements are absolutely necessary because they call out basic human rights against discrimination. They call out a very fundamental way of how we think we should be treated. LGBTQIA+ is a tag to help identify and understand ourselves better. It doesn’t change who we are as a person. It doesn’t change how technically advanced or skilled we are. It doesn’t change how we are going to perform at Amazon,” Aggarwal emphasizes.

“If the person is a good human being at heart, helps society and contributes to the general well-being of the nation, that’s what’s more important, independent of whether they are gay, lesbian, Black, white or associate themselves in any other way. Acknowledgement of this label-agnostic human existence is much more than man-made tags.”

Sheeraz Ahmad, applied scientist, Amazon SageMaker Ground Truth

Sheeraz Ahmad (pronouns: he/him) joined Amazon more than four years ago as a research scientist. Today, he works as an applied scientist on Amazon SageMaker Ground Truth team, an AWS data-labeling service that makes it easy to build highly accurate training data sets for machine learning.

Sheeraz Ahmad, applied scientist, Amazon SageMaker Ground Truth
Sheeraz Ahmad, applied scientist, Amazon SageMaker Ground Truth

Prior to Amazon, he received his PhD in computer science from the University of California San Diego (UCSD), where he focused on computational modeling of human and animal behavior in different domains, with the goals of gaining insights into the inner workings of the brain and developing behaviorally inspired machine learning models.

Ahmad, who grew up in Kanpur, India, previously earned his bachelor’s degree in electrical engineering from the prestigious Indian Institute of Technology Kanpur.

In Kanpur, Ahmad's experience was that being on the LGBTQIA+ spectrum was not well accepted, and he didn’t have many role models to follow. That changed after college when he moved to a larger city, Bangalore, and especially when he attended UCSD, where “I came across people who were out and proud and doing amazing things in life.”

Now, as an active member of Amazon’s glamazon affinity group, Ahmad is a role model himself. When he first joined Amazon, he appreciated glamazon’s support and attended events but found socializing difficult in some of the larger events. So for more than four years now, he’s organized monthly game nights, where a smaller group of glamazon members in Seattle get together to socialize and play board games. Even during the COVID-19 pandemic the tradition has continued, though online.

Pride Month is especially meaningful to Ahmad, but this year “the tone is more somber, understandably so.”

“There’s a lot going on, and as much as there is to celebrate, there’s so much more to be done. This month, as a gay man, my focus is more on being an ally for people who are going through their own struggles,” he says. “Gay men have faced discrimination and hardship, and we need to lean into those experiences, remember all the pain we’ve gone through, and be there for the womxn and our African-American brothers and sisters.

“I’m sharing with my friends, who tend to be somewhat conservative, how I have felt, based on my own experiences, and trying to relate how all members of the LGBTQIA+ community are feeling now, especially those who are African American. It’s important to be there for them, to be an ally, providing solidarity.”

“This year," Ahmad says, “feels less about celebration and more about solidarity.”

Liz Dugan, user experience researcher, Amazon Alexa

Liz Dugan (pronouns: she/her) joined Amazon earlier this year and during her onboarding experience learned about the glamazon affinity group. The voice user interface researcher, who earned a bachelor’s degree in psychology and a master’s degree in cognitive psychology from the University of Oklahoma, self-identifies as a queer, bisexual woman. She immediately felt welcomed by glamazon members.

Liz Dugan, user experience researcher, Amazon Alexa
Liz Dugan, UX researcher, Amazon Alexa

“Since I’ve been here, I’ve noted more and more people joining the group, and everyone is treated the same. People reach out and say, ‘How can we help you? Is there anything we can provide you? Please let us know if there’s anything you need.’ So you immediately feel as though this is a safe place.”

On this day, despite recent events, Dugan is more upbeat, as the Supreme Court has just ruled that a landmark civil-rights law protects gay and transgender workers from workplace discrimination. “An employer who fires an individual merely for being gay or transgender defies the law,” Justice Neil M. Gorsuch wrote for the majority in the court’s 6-to-3 ruling.

“So the LGBTQIA+ community just had a very historic win today. We wouldn’t be experiencing the moment we are today without Stonewall,” she says, referring to the 1969 New York City Stonewall riots that are considered one of the most important events leading to today’s fight for LGBTQIA+ rights.

“Everything we have today started with Stonewall, which was a riot started by trans people of color. So today we can live publicly and authentically and mostly safe from verbal abuse because of Black trans activists. Yet today we are still seeing those same populations being actively targeted and murdered without any real recourse or much publicity. Just within the past few days two Black trans women were murdered, and I’ve seen no one talk about it.”

“Some of the freedoms we enjoy today are because of Black trans women, and yet we continue to fail them as a privileged group of gay mostly white individuals, and we’re not doing enough to support the Black Lives Matter movement now. …We need to return to our roots and lift up our brothers and sisters who are suffering. They started the movement for us, and we need to be there for them now.”

Like other colleagues, Dugan feels like this year’s Pride Month is less a time to celebrate and more a time to continue pushing for progress.

“It’s a moment to return to our community’s roots. We still have problems,” she says. “We still have youth who don’t have homes and are struggling; we still have people who are discriminated against; we still have people who are being brutalized and murdered. So while we can be proud of what we’ve accomplished, we still have work to do. We have to carry our pride but still get our hands dirty. Stonewall wasn’t a celebration. Stonewall was a riot. So we have to keep fighting.”

Ruiwei Jiang, research scientist, Alexa Domains - HHO

Before joining Amazon as a research scientist, Ruiwei Jiang (pronouns: she/her) studied computational genetics in college, working in particular on human DNA. Her studies explored the adverse impact of pollution on human genetic encoding, comparing the short- and long-term effects of living in a polluted versus non-polluted environment.

Ruiwei Jiang, research scientist, Alexa Domains
Ruiwei Jiang, research scientist, Alexa Domains

“It might not sound super relevant to Alexa, but you're doing computation decks, working with a lot of data, writing code and doing a lot the analysis and building out of models, so that sort of became transferable knowledge,” she says.

Her role within the Alexa Household Organization, whose mission is to help Alexa help families stay organized and connected with one another, is to maintain the natural-language-understanding framework for features such as reminders, calendar tasks, weather, and recipes, as well as for creating models to improve customer retention.

“The world is moving towards conversational AI,” she says, “and it’s cool to be able to say you’re working in this field and developing models that are actually being used by customers, who are directly benefiting from it.”

Jiang is based in Amazon’s Vancouver office, where she’s experienced many positive actions from the glamazon affinity group, which have warmed her heart.

“They organize meetings in the office on a Sunday afternoon or Saturday morning, before the Pride parade, and hand out stickers. It’s a small thing, but it all adds up. Previous companies I’ve worked at have never really stood up as a corporation and been like ‘hey, we’re going to do something together for the Pride parade’. But at Amazon, it’s like ‘hey, let’s get together and show our support and be part of the community’, which is really inspiring.”

As a self-proclaimed ally, she can relate to the LGBTQIA+ community. “Growing up in Canada as a Chinese Canadian, I know how it feels to be to be left out and stigmatized and not feel like you're part of the group, or welcome. So I can imagine how other groups of people feel, even if I don’t have full visibility into all the problems and discrimination that they face. I think it’s important to stand up for what I think is right and not just have those values and keep it to myself.”

In light of recent events, she’s been impressed by the top-down communication at Amazon, from vice president to director level, with each leader taking the time to listen to employees and expressing their views that what’s happening to Black people in the U.S. isn’t right.

“We need to make the workplace more human than it is right now. We spend eight hours a day here, and we make friends. It’s also about keeping that diversity in hiring, which I think is one of the best ways to break down barriers, by having cross-community, cross-culture, cross-gender friendships and communications.”

Mentoring is another way Jiang promotes diversity and inclusion. “I’m what they call ‘women in tech’, and I’ve been in my career for about six years, so I think it’s important to mentor other women and girls, so they don’t feel left out or scared.”

Luyolo Magangane, applied scientist, Amazon Elastic Compute Cloud (EC2)

Located in South Africa, Luyolo Magangane (pronouns: he/him) joined Amazon just over a year ago, after a friend referred him for a machine learning role.

Luyolo Magangane, applied scientist, Amazon Elastic Compute Cloud (EC2)
Luyolo Magangane, applied scientist, Amazon Elastic Compute Cloud (EC2)

“I’m in the placement team, and we try to help customers have the best experience possible whenever they use AWS. So if a customer launches an EC2 instance, my team is in charge of the decision-making algorithm that chooses where to place that instance,” he explains.

Prior to Amazon, he studied electrical and computer engineering at the University of Cape Town and obtained a master’s degree in artificial intelligence at Stellenbosch University. He had a few jobs within the industry before joining Amazon.

He’s a member of Amazon’s glamazon affinity group, where he identifies as an ally and believes it’s important that others do too.

“Everyone should believe in the respect of the humanity of people first. When you meet someone, you have no context of their background or how they grew up. The only thing you know is that you are human, and they're also human. Your sexual orientation, gender identity, or racial identity doesn’t matter. It becomes much harder to be bigoted and to oppress someone if everyone starts from that perspective,” he says.

Magangane believes his support for the LGBTQIA+ community stems from his childhood, during which South Africa saw the end of apartheid, a system of institutionalized racial segregation from 1948 until the early 1990s.

“That was when [Nelson] Mandela was released from prison. That was when you could see the tides of change coming, from minority rule to democracy, which was incredible,” he explains.

“Every day I was encouraged to dream. And so, the benefit of being born in an environment like that led to me being born very free of prejudice. But because, historically, I come from a somewhat conservative background, I have a lot of friends and family who I care about who aren't as open minded as I think they could be.”

When he thinks about Pride and the Black Lives Matter movement and what society can learn from these events, he quotes Killer Mike, an American rapper, songwriter, actor, and activist: “It’s to ‘strategize, organize, and mobilize’, peaceful protests. It’s always done through people organizing, coming out, being peaceful, and saying that we believe what's happened is wrong and things need to change,” he says.

“I think part of that is not tolerating bigotry, which is one of the challenges you have to deal with in the Black community. You’re taught to pick and choose your battles, but you end up tolerating all those things that you don't battle, which only encourages it. You have to look bigotry in the eye and demand change. You cannot tolerate any of that. Even if institutions have to change, we’re demanding the change now.”

Shane McGarry, data scientist, Amazon Fashion

Shane McGarry (pronouns: they/them) joined Amazon earlier this year as a data scientist, focused on improving the company’s fashion catalogue using machine learning and other techniques “to create a stellar experience for our customers.”

Shane McGarry, data scientist, Amazon Fashion
Shane McGarry, data scientist, Amazon Fashion

McGarry, who identifies as non-binary, meaning they (McGarry prefers the pronouns they/them to he/she, thus the use of their, they, and them in this section) don’t exclusively identify as a man or a woman, recently earned their PhD in computer science from Maynooth University, about 25 minutes outside Dublin, Ireland, where their thesis work focused on improving the search experience within digital research environments (historical records, etc.) through visual search techniques.

Before joining Amazon, McGarry held several software development roles, where they encountered challenges.

“I’m non-binary, and I’m not traditionally masculine in any way shape or form, from my speech patterns to the way I carry myself,” McGarry explains. “What I found is that I was often ignored in ways that my colleagues with the same level of experience weren’t. When working with clients, if I dealt with them over email, they were receptive to my ideas, but when we started talking over the phone and they would hear my voice, suddenly they would become skeptical of what I was saying.”

McGarry says they encountered similar challenges with management.

“There were a lot of times when my opinion was brushed to the side, despite being proven consistently right. I would say ‘I see a problem; I think we should do this differently.’ They would ignore me, and no matter how many times I was proven right, I was never taken seriously.”

Affinity groups and diversity at Amazon

After joining Amazon, McGarry became involved in glamazon, one of 12 affinity groups within the company aimed at bringing employees together across businesses and locations around the globe. They’ve been impressed with glamazon and with their organization’s response to recent events related to the killing of George Floyd and how it’s recognizing Pride Month.

“The management within Amazon Fashion has really impressed me, especially within the past few weeks with everything that’s been occurring. …The president of our business had an all-hands meeting where she invited a global diversity and inclusion leader who has dealt with racial trauma. She talked to us about racial trauma, what it is, and how it affects people.”

Asked about lessons we can derive from recent current events, McGarry says, “In terms of the Black Lives Matter movement, it’s really important for us as individuals, as well as the company as a whole, to examine our racial biases that result from growing up in a culture that favors white people. Having a racial bias doesn’t make you a bad person. But refusing to acknowledge it, to examine it, and to work towards unlearning it, that’s where the problem lies.”

McGarry, who grew up in northeast Ohio within a deeply religious family, understands firsthand the challenges of dealing with bias and prejudice. For McGarry, Pride Month represents an opportunity to celebrate who they are without fear.

“As someone who grew up in the eighties and nineties in a deeply religious home where being gay wasn’t acceptable, and hearing messages from the community and church that gay people are evil, that God hates them, you get inundated with all of these negative messages, and you really begin to hate yourself, who you are, and you live in constant fear. So for me, Pride Month is about letting a lot of that go and celebrating yourself for who you are and really embracing it. At the same time, we have to remember our history, how far we’ve come, but yet how far we still need to go.”

Read more stories like this in our Working at Amazon section, or take a look at some of our available career opportunities in science.

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Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
At Amazon we're working to be the most customer-centric company on earth. Within the Access Points team, we do this by creating delivery experiences that delight customers, growing our worldwide network of Amazon Hub Lockers and Counters, providing away from home pickup options, and by creating new delivery initiatives that solve the changing needs of our Customers. At any Access Point, customers should expect to return, or redirect their Amazon deliveries. We measure our impact in transportation savings, revenue, and downstream customer acquisition / engagement /purchasing with Amazon.We are looking for an accomplished Manager, Research Science for Amazon Access Point’s worldwide data science team. You will define the research science direction for the team and work with our engineers to create an advanced system solving mathematically complex constraint problems. You will lead the team to own development of novel algorithmic architectures, toward the ultimate goal of accurately predicting customer purchase propensity, demand pattern and optimizing for site selection topology future Access Point locations and eligible products worldwide.Access Point has a rapidly growing customer base and an exciting science charter in front of us that includes solving highly complex algorithmic problems. You will work closely with and learn from data professionals from various disciplines (eg data engineers, analysts, machine learning engineers, economists and other fellow research scientists).Key responsibilities:· Hire, manager and grow a team of scientists and be the thought leader on the team· Collaborate with product managers and engineering teams to design and implement software solutions for Amazon problems· Contribute to progress of the Amazon and broader research communities by producing publications· Be hands-on when needed, to mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve forecast accuracy or optimization performance
US, PA, Pittsburgh
Amazon is looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.As a Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. 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 spoken language understanding.We are hiring in all areas of spoken language understanding: ASR, NLU, text-to-speech (TTS), and Dialog Management.Position Responsibilities:· Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.· Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.· Routinely build and deploy ML models on available data.· Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.
CA, ON, Toronto
Amazon Sponsored Ads is one of the fastest growing business domains and we are looking for talented scientists to join this team of incredible scientists to contribute to this growth. We are still in Day 1 and there is an abundance of opportunities that are yet to be explored. We are a team of highly motivated and collaborative team of machine learning and data scientists, with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, and we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Sponsored Products (SP) Bids and Budgets team is focussed on helping advertisers set their campaign bids and budgets in an optimized fashion.As an Research Scientist on this team you will:· Build machine learning models and utilize data analysis to deliver scalable solutions to business problems.· Perform hands-on analysis and modeling with very large data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders.· Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's marketplace.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Research new predictive learning approaches for the sponsored products business.Why you love this opportunityAmazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering 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 highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career GrowthYou 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 fundamentally 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, MA, Cambridge
Alexa is Amazon’s intelligent cloud-based voice recognition and natural language understanding virtual assistant. We’re building the speech and language solutions behind Amazon Alexa and other Amazon products and services. Come join our team and help improve the customer experience for the growing base of Alexa users!The Alexa Artificial Intelligence (AI) team is seeking a talented Applied Scientist to build ML models to detect issues that end-users have in their interactions with Alexa (defects and their possible root causes). These models are then used to monitor trends over time with Customer Experience (CX) metrics, guardrail metrics in weblabs, setting defect reduction goals, and defect discovery and resolution.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the hiring groupAlexa AI is an analytics and science team within Alexa. Our mission is to provide an understanding of the customer experience that allows Alexa teams to improve system performance and customer engagement. Our primary deliverables are CX metrics, analytics tools, and customer insights.Job responsibilitiesAs an Applied Scientist with our Alexa AI team, you will work on assessing Alexa's performance using predictive ML models. You will build and improve models to classify Alexa’s responses as correct/incorrect, and predict the most likely cause of failure in cases of incorrect action. Your work will directly impact our customers in the form of products and services that make use of speech and language technology, particularly in developing predictive models to continuously improve the Alexa experience for our customers.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
LU, Luxembourg
Are you a talented and inventive engineer with strong passion about Artificial Intelligence and Predictive Modeling? Would you like to develop Machine-Learning tools by playing a key role within EU RME Predictive Analytics team? Our mission is to drive the Predictive Maintenance (PdM) and Spare Parts (SP) programs for Amazon EU Operations that consists of complex automation, sortation, robotic and materials handling systems.As Machine Learning Tool Specialist you will be working with large distributed systems of data and providing predictive maintenance expertise for over 2000 maintenance engineers, managers and administrators by supporting the entire network managed by EU RME, which may include non-EU locations (such as Singapore, Australia and Japan). You will connect with world leaders in your field and you will be tackling ML challenges by carrying out a systematic review of existing solutions. The appropriate choice of the ML methods and their deployment into effective tools will be the key for the success in this role.The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices.Key Areas of Responsibilities:· Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement· Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime· Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares
LU, Luxembourg
Are you a talented and inventive engineer with strong passion about Artificial Intelligence and Predictive Modeling? Would you like to develop Machine-Learning tools by playing a key role within EU RME Predictive Analytics team? Our mission is to drive the Predictive Maintenance (PdM) and Spare Parts (SP) programs for Amazon EU Operations that consists of complex automation, sortation, robotic and materials handling systems.As Machine Learning Tool Specialist you will be working with large distributed systems of data and providing predictive maintenance expertise for over 2000 maintenance engineers, managers and administrators by supporting the entire network managed by EU RME, which may include non-EU locations (such as Singapore, Australia and Japan). You will connect with world leaders in your field and you will be tackling ML challenges by carrying out a systematic review of existing solutions. The appropriate choice of the ML methods and their deployment into effective tools will be the key for the success in this role.The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices.Key Areas of Responsibilities:· Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement· Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime· Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares
US, WA, Seattle
Do you want to join Alexa Artificial Intelligence (AI), the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join the Alexa AI team, which is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.A day in the lifeAs a Senior Applied Scientist, you will be working alongside a team of experienced machine/deep learning scientists and engineers to create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..About the hiring groupThe Alexa AI team is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.Job responsibilitiesYou will be expected to:· Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT).· Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpoints· Create and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenarios;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel machine learning and deep learning algorithms and models.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
The Fresh Food Fast organization is responsible for transforming the online and offline grocery experience for Amazon. We are seeking a senior science leader to define our long-term science vision, build out a high-performing team and deliver business critical scientific models to increase customer engagement, inform long-term investment decisions, and measure how grocery is contributing to Prime and Amazon.A day in the life· You will influence senior leaders (VP+) across business, product, finance, and engineering functions and you will partner closely with central Amazon teams to pioneer new models to measure grocery’s future impact to Prime and Amazon.· You will manage a team of Data Scientists, Economists and BIEs to deliver results on behalf of customersAbout the hiring groupWe’re a team of Product Managers, Data Scientists, Economists and Business Intelligence Engineers focused on deeply understanding how F3 customers engage with physical and online grocery stores in order to enhance their shopping experience, drive engagement and loyalty, and measure their long-term impact to Amazon.Job responsibilitiesYour team will apply complex scientific methods to challenging business problems including, “How can we encourage customers to shop more frequently?”, and “how should we measure the impact of physical store expansion and technology innovation in those stores (e.g. Just Walk Out Technology)?”. You will power through ambiguity, finding the right solutions to problems and influencing others to align with your approach and help drive results. You will mentor and develop scientists to achieve their goals, raising the bar technically and driving scale and efficiencies to better leverage our data and technologies.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
DE, BE, Berlin
Our mission is to build the automated intelligence supporting critical service operations at global scale. The Intelligent Cloud Control Machine Learning (ICCML) team works to automate complex large-scale operations of Amazon’s consumer services by developing data-driven, scalable, and seamless solutions available to customers and ICC partners. We employ machine learning to reduce system and information complexity while improving service reliability. We invent practical approaches within application areas such as anomaly detection, time series analysis, classification, causal inference, and text mining, and we apply the latest and most sound techniques of probabilistic modelling, estimation, deep neural networks, and natural language processing (NLP). Working with us offers exciting challenges where you will grow as an applied scientist and technical leader, combining your scientific and engineering skills to solve complex machine learning problems together with our tech teams around the world.As an Applied Scientist of the ICCML team, you will have the important role of mapping business problems to high-impact solutions. You will turn theoretically sound methods into practically applicable models designed for processing massive volumes of data in large-scale environments. You will define business relevant solutions implemented as end-to-end machine learning functions and data processing pipelines that integrate with our partners production systems. In a fast-paced innovation environment, you will work closely with our Applied Scientists, Machine Learning Engineers, and partners to design machine learning models and experiments at scale. You dive deep into all aspects of the practical machine learning development cycle, encompassing sound use of data pre-processing techniques, analysis, modelling, and validation methods. You master the complex theory under the hood of machine learning and you keep up to date with the latest scientific development in information processing, modelling, and learning methods. You take lead of the scientific and technical work in cross-team collaborations with the ultimate objective of creating a delightful experience for our customers using our services.
IL, Tel Aviv
You: Alexa, I am looking for a new career opportunity, where I could conduct applied research, impact millions of customers, and publish about it in top conferences. What do you suggest?Alexa: The Alexa Shopping team is looking for brilliant applied researchers to help me become the best personal shopping assistant. Do you want to hear more?You: Yes, please!Alexa: As an applied researcher in the Alexa Shopping Research team, you will be responsible for research, design, and implementation of new AI technologies for voice assistants. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will inventing, experimenting with, and launching new features, products and systems. Ideally you have a expertise in at least one of the following fields: Web search & data mining, Machine Learning, Natural Language Processing, Computer Vision, Speech Processing or Artificial Intelligence, with both hands-on experience and publications at top relevant academic venues.