Headshots of female Amazon scientists participating in the Grace Hopper Conference.
Amazon scientists (from top left) Kristine Brown, Laura De Lorenzo, Yang Liu, Hannah Marlowe, Nina Mishra, Candace Thille, and Chao Wang provide their perspectives on what it will take to attract more women to pursue STEM careers.
Credit: Stacy Reilly

Seeds of inspiration

Given the recent death of US Supreme Court Justice Ruth Bader Ginsburg, and with the Grace Hopper Celebration taking place this week, we asked Amazon women scientists what it will take to attract more women to pursue STEM careers.

The AnitaB.org Grace Hopper Celebration, an event honoring Grace Hopper’s legacy by inspiring future generations of women to pursue careers in technology, takes place this week, as it has every year since 1994. Amazon is a Diamond sponsor of this year’s event.

Unlike previous years, though, this year’s celebration, which AnitaB.org produces in partnership with the Association for Computing Machinery (ACM), will be held virtually given restrictions related to COVID-19.  What hasn’t changed is the vision of AnitaB.org: a future “where the people who imagine and build technology mirror the people and societies for whom they build it.”

Based on the latest statistics from the National Center for Women & Information Technology, that future is still on the horizon. While 57 percent of US professional jobs were held by women in 2019, just 26% of professional computing jobs were occupied by women. Among the 26% of women occupying professional computing jobs, 7% were Asian women, 3% Black women, and 2% Hispanic women.

Elizabeth Nieto, Amazon’s head of global diversity and inclusion, says the company’s vision is to create a culture where the best builders, including women from all backgrounds, want to work and stay at Amazon “because they are drawn to our mission, our culture, and our leaders. We are focused on being globally inclusive and creating a culture at Amazon where everyone can reach their full potential.”

At last year’s event, Brenda Darden Wilkerson, president and CEO of AnitaB.org, told nearly 25,000 attendees, “I want our daughters to say, ‘I heard back in the day there was this problem that there weren’t enough women in tech.  What was that like?’”

In advance of this week's conference, Amazon Science asked some of the company’s women scientists when they think the industry will reach that goal, what it will take to get there, and who or what most inspired them to pursue their science careers.  Below are their responses.

Kristine Brown is a principal economist within Amazon’s human resources organization. She obtained her PhD in economics from the University of California, Berkeley.

Kristine Brown
Kristine Brown

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

At Amazon, I learned the importance of continuous inspection to identify opportunities for improvement, and to adapt to a shifting environment. I think the same applies here; the task of deliberately creating opportunities for others, and removing barriers to shape a more equitable and inclusive workplace will evolve over time, but it doesn’t have an end date.

Q. What will it take to get there?

The demand for science and tech talent is increasing in the traditional technology sector and in other industries that are leveraging new technologies and data to provide better services and products. The door is wide open, but you can’t walk in if you don’t know it exists, or how to get there. For me, early exposure and encouragement to explore science and math were critical. I discovered a passion for physics and that interest pushed me to develop my math and science skills. I was lucky to have this opportunity. Casting a wider net to provide early, low stakes opportunities to engage in science and tech activities, develop STEM skills, and learn about the diversity of work in this space, will help demystify the technology industry. It will also allow kids and young adults to learn whether it matches their interests and whether they have a knack for it.

Q. Who or what inspired you most to pursue your STEM career?

My fascination with the natural world was fueled by observing wildlife, peering through an observatory telescope at distant planets, and nature magazines with beautiful photos. The mind-bending questions of space and time were especially irresistible; I wanted the answers to the universe, and physics and math were the key to finding them. Later, as I became interested in understanding human behavior (which I’d argue is no less mysterious) and how government policies could improve lives, I found economics came with a familiar toolkit of mathematical modeling and scientific testing to answer these questions. I saw a career in economics as an opportunity to leverage my strengths to drive positive change.

Laura De Lorenzo is a quantum computing research scientist within the Amazon Web Services organization. She earned her PhD in applied physics from the California Institute of Technology (CalTech).

Laura De Lorenzo
Laura De Lorenzo

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

To be honest, I'm so uncertain as to be unwilling to hazard a guess, but I do think it is a long way off. In some STEM fields, such as medicine, the gender gap has nearly, or completely, closed within the past 50 years. In other fields, the percentage of women (measured by employment or educational degree) remains far below 50% and doesn't appear to be changing significantly year over year. The amount of progress in some fields is encouraging, but it's difficult to understand why fields like physics and computer science lag behind.  

Q. What will it take to get there?

This issue is clearly challenging and multi-faceted, so I cannot offer a single simple solution. However, I think one important aspect is a focus on young women, in the middle school to high school age group. For example, women are already underrepresented in the high school AP physics examinations. By the time students reach the undergraduate level, only about 20% of physics majors are female. I think it is essential to understand why young women make these choices. Is it a lack of role models, or self-doubt about their ability to perform well in science, or peer pressure, or something else entirely?  In the meantime, I think it is important to offer encouragement and support to young students because once women drop out of the STEM fields, it is more difficult for them to return at a later age.

Q. Who or what inspired you most to pursue your STEM career?

From a young age, my parents were always supportive of my interests in science and math, and of my career in general. My mother went to medical school in the late ‘70s, when women represented only about 20% of medical students in the US.  I always saw her as strong, hard-working, and independent, and she was a great example for me to follow. Both of my parents had high expectations for me and would never allow me to perform at less than my best. I definitely owe the largest debt of gratitude to them. However, programs such as Science Olympiad and the Pennsylvania Governor's School for Science (a five-week program for rising high school seniors), also helped me by introducing me to a peer group with similar interests, and to a larger group of role models and mentors who could help me navigate the next step.

Yang Liu is a principal scientist within the Alexa AI organization. She earned her PhD in electrical and computer engineering from Purdue University.

Yang Liu
Yang Liu

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

Maybe in another generation. My daughter is in first grade now. I’m hopeful we can reach that day when she finishes high school, and is choosing a college major or planning a career in STEM or the technology industry.

Q. What will it take to get there?

It will require effort from everyone in society, including educators, students, parents, and policy makers. Starting from kindergarten through high school, young girls and women need support and encouragement from parents and teachers to realize their potential and get excited by STEM careers; educators need to nurture girls’ interest in STEM and create an environment to help them do well in these subjects; and policy makers need to provide appropriate and adequate resources for teachers and students. As Hillary Clinton has written and said, it will take a village for society to address existing biases and prejudices. But with everyone’s effort, I’m confident we can get there by the time my daughter is entering the workforce.

Q. Who or what most inspired you to pursue your STEM career?

Mostly just people around me — my family, teachers from elementary schools all the way up to universities, and an overall supportive environment, including friends and peers. I grew up in China. My mom was a math teacher, and I did well in math starting in elementary school. All I got from everyone around me was support, respect, and encouragement to continue to excel in this subject. I never encountered an attitude like “girls are not good at math (or other science subjects) or don’t need to do well in math”. I made many friends (girls and boys) in schools, and was never left out because I did better than others in science. Reflecting on this, there’s no doubt I benefited from that supportive environment, leading to my future career in STEM. I don’t know for sure if there is a difference between China and US; I don’t have enough sample to draw a conclusion. I’m not even sure if there’s been a generational change within China. What I can say is that I would encourage girls and young women to pursue STEM careers.  The subjects themselves are fascinating. Right now I’m working within the Alexa organization on making computers and other devices “intelligent” by recognizing speech and understanding human language. The work is challenging, interesting, and it’s great to see how Alexa can have a positive impact on the lives of our customers. 

Hannah Marlowe is a senior data scientist within the AWS Worldwide Public Sector Professional Services Data and Machine Learning team. She earned her PhD in physics from the University of Iowa, specializing in the study of astronomical X-ray sources and space-borne instrumentation development.

Hannah Marlowe
Hannah Marlowe

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

The university building where I completed my PhD was an interesting time-capsule to observe some of the progress of women in physics and astronomy. The eight-level physics building, built in the ‘60s, originally featured only men’s restrooms apart from one. The lone women’s restroom was located across the hall from the administration office and included an attached kitchen (still there today), presumably so that secretaries working in the office could prepare meals during the work day. In years since, they have thankfully adjusted the restroom situation, but the basement where my team’s lab was located still only had a men’s room and it was always an interesting reminder of that past.

Today, the thought of designing a building with facilities only for men (much less a public university building) seems completely ridiculous, but it wasn’t so long ago that it apparently made practical sense. We are standing on the shoulders of giants like Ruth Bader Ginsburg and other advocates of gender equality who paved the way for the participation of women in traditionally male-dominated fields and shifted public perception of what women can and should do. It is my hope that we continue to build on the work they championed, but it will take a concerted effort. I don’t have a good answer for when I think we will get to the point that gender disparity in STEM fields is a distant memory. However, I have seen positive changes and witnessed shifts over my own career (not limited to restroom design choices) that make me optimistic that we can get there eventually.

Q. What will it take to get there?

I don’t believe there is any one right answer, but one of the most important things is making it clear to young girls and women that they belong and add value in STEM. I think people tend to gravitate to careers and roles that they have exposure to, and where they see role models that look like themselves. The other piece is not just encouraging girls and women to explore STEM, but expecting it and treating it like a normal career path versus an exceptional one. That is not to say we should be pushing girls to pursue something they aren’t interested in, but I hope that we get to a point where girls pursuing STEM seems completely boring and commonplace. That gets easier as more women enter STEM fields, and I think there is probably a tipping point where women and girls just naturally begin to gravitate in larger numbers to these fields. As a practical matter, we should also be equipping girls with all of the skills and tools that will make them successful in these fields from a young age. Anyone who isn’t exposed to math and science early is going to have to play catch-up later on, and may question their own abilities when they compare themselves to peers who have been in advanced math and science tracks throughout grade school.

Q. Who or what most inspired you to pursue your STEM career?

I feel extremely fortunate that I have mainly been able to follow my interests and what I found to be fun and personally challenging throughout school and my career so far. I also had many great influences and mentors in my life that helped me along my path. From an early age my father used to point out constellations in the sky and took my sister and me to observe comets and space shuttle launches. Once I got to high school, I had a wonderful retired NASA engineer as a physics teacher who introduced me to physics and to Carl Sagan and helped us start the first astronomy club at our school. For my undergraduate education, I chose a small women’s liberal arts college, Agnes Scott College, that had its own observatory and offered an astrophysics degree. At Agnes, I had excellent professors and the unique experience of having all of my STEM peers be women. I think that experience especially helped inoculate me for the future where I’ve more often found myself the only women in large lab groups, collaborations, and professional teams.

The last thing I would like to mention here, because I think it is really important and something I have often struggled with, is the issue of self-doubt. Self-doubt and imposter syndrome are definitely not limited to women in STEM fields, but I think being the only one around who looks like you can contribute to those feelings, and can push people away who have wonderful things to add to these fields. I have so often questioned myself and my worthiness, intelligence, and value (did I really earn that award/fellowship/job offer or was I selected just because I am a women/was in the right place at the right time/completely by mistake?). It was really important for me to know that I was not alone in doubting myself and my capabilities and I am grateful to colleagues and mentors, men and women alike, who shared their own experiences with self-doubt and imposter syndrome along the way. I’ll always remember my wonderful, brilliant, and inspiring undergraduate professor telling me about her own struggles in graduate school, and that one of the reasons she became a professor was to show us that “if she could do it, any of us could.”

Nina Mishra is a principal scientist Amazon’s Health and Wellness organization. She earned her PhD in computer science from the University of Illinois at Urbana-Champaign.

Nina Mishra
Nina Mishra

Q. When do you think we’ll reach that day that Brenda Wilkerson talked about last year?

While computer science has had a gender gap since its inception, I was convinced early on that a trifling matter like gender difference would self-correct. I was wrong. According to a 2019 Taulbee survey, 80% of PhDs are awarded to men and 20% to women. Back in 2001, the split was 78%/22% -- essentially unchanged after 18 years. The problem is not likely to improve in the next five years since the 80/20 gap persists in 2019 at the computer science bachelor’s degree level. Beyond gender gap, there is a gaping wide race gap. In 2019, less than 1% of PhDs were awarded to Black or African-American students; in 2001 this number was 1.3% -- again, essentially the same.  This gap persists early in the education pipeline.  For example, while Atlanta’s population is more than 50% black, only 3 Black students are enrolled in advanced placement computer science courses in local public high schools -- that is 3 out of 528,000! Narrowing this gap is critical for the technology industry. Companies do not want the lack of diversity in their workforce to perpetuate into their products. When will we reach that day? When we change the computer-science culture to welcome and embrace differences. 

My hope, adapting the words of others, is that the arc of social justice is long, but bends towards equality.
Nina Mishra

Q. What will it take to get there?

We cannot reach parity until we overturn the presumption that women hold different roles than men. Until we eliminate the idea that there are ‘girls’ disciplines’ and ‘boys’ disciplines’, and slights such as asking a woman in a meeting if she’s a secretary, or if she can get water for the meeting, it will be difficult to make progress.  Derogatory comments like these contribute to the ‘million cuts’ that women experience and can ultimately lead people to pursue careers where they are more wanted. I’m surprised that people are still hung up on these role associations, but the concern is real and people like Ruth Bader Ginsberg fought their entire career to overturn them. My hope, adapting the words of others, is that the arc of social justice is long, but bends towards equality.

Beyond reaching parity, underrepresented groups need to be seen and more prominently heard. All people have amazing ideas, but I have repeatedly seen ideas from underrepresented groups diminished and even discarded. When such ideas later resurface with the ownership transferred to someone in an overrepresented group, the process is demoralizing and influences people to find alternate careers. These injustices need to be reported and escalated to higher levels. The problem can only be fixed if we have an active dialogue starting from a young age.

Accessibility of resources is a consideration in some parts of the country. There are still households where students do not have a computer and others where a single computer is shared among many family members. There are households that do not have internet access. And, there are parts of the country where computer science classes and teachers aren’t available to students. People cannot choose a computer science career if they are missing these simple, starter ingredients.

Outreach is another area where we can do more. Students may wonder, `What will I do if I have a career in STEM?’. Everyone knows what a medical degree or a law degree leads to career-wise, but what does a computer science degree lead to? The common misperception is of macho geeks cranking out tons of code. For me, it is about finding ways to use data collected about some people to help millions more. It is about the amazing predictions that machine learning can make. The way that smartwatches can detect heart arrhythmias and search engines connect people to information is rooted in data and machine learning. Writing code is a means to that end. Novel and crazy ideas are what push the field forward. A more concerted effort is needed to communicate this to young students.

Q. Who or what most inspired you to pursue your STEM career?

My mother played a huge role early in life. She has a gift for explaining mathematical concepts. She taught math at a community college and also a prison. Later on, my high school math teacher played a large role. She forced students to walk to the board and write/explain their solutions. It was an early peek into the clarity one achieves by teaching their solution to others. Both taught me the precision and beauty of math. Both insisted on exacting standards for the highest quality of work. My father taught me to be bold. He has a PhD in inorganic chemistry and emphasized scientific innovation. To this day, he shares articles with the latest and greatest scientific findings, always pushing me to aim higher.

Candace Thille is director of learning science within Amazon’s Global Learning and Development organization. She obtained her master’s degree in computer science from Carnegie Mellon University and earned her PhD in education from the University of Pennsylvania.

Candace Thille
Candace Thille

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

I am going to change the question to respond to what I wish Brenda Darden Wilkerson had said: “I want our sons to say ‘I heard back in the day there was this problem that there weren’t enough women in tech. What was that like?” I do not mean to imply that the quote needs to be changed because the problem is only important if it is acknowledged by our sons, but rather that the problem will only be corrected when the problem, and the responsibility for correcting it, is owned by our sons too, not just our daughters.  When will we reach that day?  When gender is no longer seen as a feature of an individual that is relevant for encouraging, allocating, or selecting roles and responsibilities.

Q. What will it take to get there? 

First, an acknowledgement that the current systems and structures in STEM fields are grounded in the idea that gender and race are features of an individual that are relevant for encouraging, allocating, or selecting roles and responsibilities. Second, a commitment to ongoing inspection of those systems and structures for biases in order to change them. People would sometimes ask Ruth Bader Ginsberg “When will there be enough women on the court” and she would reply, “When there are nine”.  She would say then that “People are shocked, but there’d been nine men, and nobody’s ever raised a question about that”.  

Q. Who or what most inspired you to pursue your STEM career?

I have always been fascinated with how things work, both for the joy of understanding and to figure out how to make things work better. I have been awed by the discoveries that come from good research, and from the positive impact of using the results from research to make the world better. Both as an academic researcher and as a research scientist at Amazon, I situate my work in Pasteur's quadrant and work on projects that seek fundamental understanding of scientific problems, while also having immediate use for society.

Chao Wang is a senior applied science manager within the Alexa organization. She earned her PhD in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT).

Chao Wang
Chao Wang

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

I’m reminded of the Bill Gates quote, “We always overestimate the change that will occur in the next two years, and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.” I’d like to think we could reach that state within the next 10 years, but it will probably take another generation of change. So I think closer to 2050.

Q. What will it take to get there?

I’ll share a very different perspective. I grew up in China and the education system back then made everyone decide their major in sophomore year of high school. That system channeled students to different college entrance exams depending on the choice (so your career paths are largely determined very early on). It was a 5:2 split ratio for STEM and non-STEM (probably matching the college admission ratio), and naturally only students who were really interested in a non-STEM career path self-selected into that track. The majority chose STEM. At the time, I did notice that more female students chose the non-STEM track, but plenty of us ended up in the STEM track, too (strength in numbers). I have observed that in the US, if you are ambivalent about STEM, then the gender stereotype works against young women pursuing STEM careers. I contrast that with the early days of computing in the US, when computer programmer was considered a female job, and you had a lot of female programmers in an otherwise male dominant technology industry and computing pioneers like Dr. Grace Hopper. It all changed (for the worse) within a generation, and we can change it back with the right societal mental shift.

Q. Who or what most inspired you to pursue your STEM career?

Growing up in China I never felt that STEM was somehow an unusual choice for a young woman. Math and physics were always my favorite subjects, and no one ever discouraged me from pursuing those interests. I enjoyed the problem solving of math and physics much more than courses requiring writing or memorization. I opted for the STEM track in high school and was admitted into a top engineering school in China for my undergraduate studies. My career path was more or less decided from that point in time.

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We are seeking a Senior Applied Scientist to focus on Robotics Spatial Intelligence and Semantic Understanding. In this role, you'll research and build advanced semantic and world understanding algorithms that enable robots to observe, understand, and reason about complex and dynamic home environments. You'll work across a broad spectrum of 3D perception, contextual understanding, and world modeling approaches to build robust solutions that support autonomous decision making, task planning, navigation, and manipulation. Key job responsibilities - Develop and implement robust World Understanding and Modeling algorithms for a domestic robot. - Build simulation-based and on-robot evaluation frameworks with comprehensive benchmarks and metrics for systematic evaluation of Our Spatial Intelligence stack. - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world performance. - Collaborate with navigation, manipulation, and other teams to ensure seamless integration of World Understanding capabilities. - Stay current with the latest advances in World Modeling, Spatial Reasoning, and related fields and apply relevant findings to improve system performance About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
GB, London
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies — all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business — available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video, in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through checkout and beyond. We operate at scale across hundreds of millions of customers, and we are expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team in London. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a delivery focused role within a high-visibility multidisciplinary group of engineers and scientists, focused on improving the customer experience for Prime Video. Key job responsibilities - Research, design, and implement machine learning approaches (e.g. reinforcement learning and recommendation systems) that personalise across different customer touch points. - Collaborate with engineers to deploy and integrate successful experiment results into large-scale, complex Amazon production systems with low latency. - Design and execute rigorous experiments to demonstrate the technical efficacy and business value of your methods. - Act as a subject-matter expert and help define the science roadmap and research agenda in line with organisational priorities and production constraints. - Provide machine learning thought leadership to technical and business leaders, thinking strategically about business, product, and technical challenges. - Work with technical product managers to work backwards from what matters to customers and deliver ML-backed solutions. - Share results with the team and wider scientific community through documents that are both statistically rigorous and compellingly relevant. A day in the life You will be a research leader and innovator within the Commerce Insights organisation. You will collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines, balancing competing objectives, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer-experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.
US, WA, Seattle
AWS is seeking a Data Scientist to join our Consumer Experience team. We're looking for someone who is curious about large-scale data challenges and motivated to apply ML/AI and advanced analytics to solve real customer problems. Reporting to the Science Lead, this role supports the analytical capabilities of AWS by applying emerging technologies, contributing to data science best practices, and partnering with stakeholders to deepen understanding of the AWS customer experience. The ideal candidate has experience working in complex data and analytics environments, with hands-on skills in Machine Learning and advanced analytics techniques — preferably within cloud computing. You should be comfortable building models and equally comfortable explaining what they mean to a non-technical audience. Key job responsibilities - Apply statistical analysis, ML, and complex data mining techniques to deliver insights that shape the AWS customer experience. - Build production-ready analytics solutions that support effective business decisions. - Partner with product, engineering, and design teams to develop and launch data products. - Contribute to the adoption of Gen AI and ML-based approaches across the data science practice, including building proof-of-concept AI-driven customer use cases. - Communicate technical findings clearly to both technical and non-technical stakeholders. - Bring new ideas — share approaches, tools, and methods that raise the bar for the team.
IN, KA, Bengaluru
Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide. Key job responsibilities • Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models. • Strong foundation in machine learning and statistical modeling. • Experience deploying ML solutions into a production environment. • Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes. • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions. • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries. • Willing to travel to Mexico (MX) and Brazil (BR). • Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams. About the team You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.
IN, KA, Bengaluru
Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide. Key job responsibilities • Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models. • Strong foundation in machine learning and statistical modeling. • Experience deploying ML solutions into a production environment. • Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes. • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions. • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries. • Willing to travel to Mexico (MX) and Brazil (BR). • Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams. About the team You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.
US, CA, Santa Monica
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Santa Monica, California Job Number: AMZ10564053 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, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, NY, New York
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! Demand Tech Experience (DTx), as an org, owns the Amazon Ads Console — the advertiser-facing workspace for creating and managing campaigns, reviewing performance, and acting on recommendations used by millions of advertisers. Our team, DTx Science, is the centralized science team within DTx, dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. We leverage a breadth of state-of-the-art techniques such as deep learning, reinforcement learning, LLMs, long term causal modeling as well as sophisticated A/B testing to develop personalized experiences with directly quantifiable business and advertiser success outcomes. We are looking for an accomplished machine learning expert to lead the Applied Science strategy for our recommendation creation and personalization program. In this role, you will work closely with business leaders, stakeholders and cross-functional teams to drive program success through ML-driven solutions. You will shape the applied science roadmap, promote a culture of data-driven decision-making, and deliver significant business impact for millions of advertisers worldwide and the company using advanced data techniques and applied science methodologies. Key job responsibilities As an Applied Scientist on this team, you will - Serve as the technical leader in Machine Learning, driving collaboration between scientists and engineers, guiding efforts within the team and collaborating with other teams. - Conduct hands-on analysis and modeling of large-scale data to generate insights that boost ads revenue while maintaining a positive advertiser and shopper experience. - Lead end-to-end Machine Learning projects that involve high levels of ambiguity, scale, and complexity. - Build, experiment, optimize, and deploy machine learning models, collaborating with software engineers to bring your models into production. - Run A/B experiments, gather data, and perform statistical analysis to validate your models. - Develop scalable and automated processes for large-scale data analysis, model development, validation, and serving. - Explore and research innovative machine learning approaches to push the boundaries of what’s possible. About the team The DTx Science team is the centralized science team within Demand Tech Experience (DTx), dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. Improve advertiser experience: Ad Console Personalization and Recommendation Development aim to improve the advertiser ad console experience by providing customized and personalized console experiences for each user and increasing their ad performance with relevant, science-backed onboarding and oppertunty recommendations. Protect advertiser experience: Ad Console Experimentation and DVA Agent Evaluation and Impact Measurement protect the advertiser experience by supporting the organization in making data-driven decisions to launch UX and agentic features that benefit advertisers.