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A grid of 12 women scientists who were asked what three steps we can take as a society to forge a more gender-equal science community
As International Women's Day approached, we asked women scientists from research areas across the company what three steps we can take as a society to forge a more gender-equal science community.
Credit: Glynis Condon
Sustainability

How to forge a more gender-equal science community

International Women's Day is March 8 with the theme: #ChooseToChallenge.

By Staff writer
March 04, 2021
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International Women’s Day (IWD) is March 8, 2021. The day celebrates the social, economic, cultural and political achievements of women, and also denotes a call to action to accelerate gender parity. This year’s theme: #ChooseToChallenge.

International Women's Day logo

“From challenge comes change, so let’s all choose to challenge,” says the IWD website.

As IWD approached, Amazon Science asked women scientists from research areas across the company what three steps we can take as a society to forge a more gender-equal science community. Below are their responses.

Bouchra Bouqata

Bouchra is a senior applied scientist within Amazon Robotics. She earned her PhD in machine learning and artificial intelligence from Rensselaer Polytechnic Institute.

Bouchra Bouqata
Bouchra Bouqata

  • Provide a clear pipeline for advancing and promoting women’s careers in science. Companies and institutions should adopt gender-balanced peer review promotion processes and committees.  They should also provide special funds and grants to help women scientists further their research and work.
  • Conferences and publishing venues should adopt gender-conscious peer-review committee, and speaker- selection committee recruitment processes.
  • Companies and institutions should commit to educating everyone, not just leadership, to combat the issues facing women in science. They should provide gender awareness training as a standard component of any training they provide to their employees and members. They should provide seminars and convene roundtable discussions on gender issues in science to facilitate communication and identification of solutions.

Nilay Noyan Bulbul

Nilay is a principal scientist within the company’s Supply Chain Optimization Technologies organization.  She earned her PhD in operations research from Rutgers University.

Nilay Noyan Bulbul
Nilay Noyan Bulbul

  • Call the gender disparity out: Identify where women scientists are marginalized, and call out the disparity to ensure fair representation at the leadership of scientific research and decision-making, as well as “invite-only” prestigious roles, such as keynote speaking engagements, prize juries, and journal editorial board memberships.
  • Invest in the future: Create more initiatives and opportunities for the next generation of women scientists via mentoring and targeted prize and research fund calls.
  • Keep everyone accountable: Make sure every entity working towards gender equality in science community has a tangible way to measure the “change” and keep track of the progress, and make the process transparent.”

Cindy Cui

Cindy is a senior economist within the Alexa Shopping organization. She earned her PhD in economics from the University of Texas at Austin.  

Cindy Cui
Cindy Cui

Role models, aspirations, and supportive community are most important factors to me. Growing up, my grandma taught me reading and math. I still remember the days when we would go through math problems and I felt happy and proud when I solved them correctly.

My grandma is also one of the few female teachers in her generation and always emphasizes the importance of education and hard work. In school, many smart female classmates encouraged and challenged me throughout.

It takes all of us to improve gender equality in science, doing our best and helping others along the way.

Donna Dodson

Donna is a senior principal technologist within the AWS Security organization. She earned her master’s degree in computer science from Hood College.

Donna Dodson
Donna Dodson

  • Build a culture that values deep thinkers who balance speaking and listening to others. Often the subculture’s voices — including women’s — are not heard.
  • Create compensation, incentives, benefits, resources, recognition and a flexible workplace that balance needs at different stages in life. Early- and mid-career scientists with families require flexibility for a work-life balance.
  • Recognize and promote diverse voices throughout K-12 science programs to empower girls to grow their confidence in science knowledge, skills and abilities

Maryam Fazel-Zarandi

Maryam is a senior machine learning scientist within the Alexa AI organization. She earned her PhD in computer science at the University of Toronto.

Maryam Fazel-zarandi
Maryam Fazel-Zarandi
Credit: Pierce Harman Photography

I have been able to pursue my dream of becoming a scientist and have had access to role models and mentors throughout my education and career. The number of women scientists like me has increased over the past decades, however, we are still far from a gender equal science community.

While we should continue to reduce the large gap that still exists in terms of numbers, in my opinion, we should put more focus on mechanisms to retain women scientists. Lack of support for women in difficulty, feelings of isolation at work, and unmet expectations are among the top reasons why women leave their careers in science. The COVID-19 pandemic has further contributed to these difficulties as more women are taking additional caregiver roles at home, which in turn impacts their continued employment and career advancement.

To forge a more gender-equal global science community, we need to promote women’s integration in the research environment and workplace by learning about women’s experiences and providing direct support for women in difficulty. Our institutions and organizations should also implement and monitor measures to ensure womens’ career development in a post-pandemic world.

Rashmi Gangadharaiah

Rashmi is a senior research scientist within the AWS organization. She earned her PhD in information technology, artificial intelligence, and machine learning from Carnegie Mellon University.

Rashmi Gangadharaiah
Rashmi Gangadharaiah

As a woman and a mother of two girls, I’m glad that gender equality has been receiving more attention. Just talking about gender equality doesn’t mean that we’ve created a gender-equal community. Here are three steps that we can take to create a gender-equal global science community.

  1. Create opportunities that encourage more women to tackle challenging projects.
  2. Recognize women who have an impact on projects and give credit where it’s due.
  3. We as women should not be afraid to take on challenging projects, grab opportunities that come our way and have a community/support system when the deck is stacked against us.

Antia Lamas-Linares

Antia is a principal research scientist within the AWS Center for Quantum Computing. She earned her PhD in physics from the University of Oxford.

Antia Lamas-linares
Antia Lamas-Linares

Helping diversify science is often not about actions within science, but immediately around science; removing the “death by a thousand cuts” problems.

The most impactful action we can take to improve science careers for women is to prioritize affordable childcare in research campuses (both university and industrial). This also has the very nice feature of benefiting the whole community of researchers, but it would have a disproportionate effect on women, while avoiding the insidious problems of preferential treatment.

If we can make space in campuses for exercise and culture, we can make space for daycares. A second thing we could do is prominently feature female scientists without remarking on their gender, they should not be an anomaly that needs to be highlighted and this narrative can be gently pushed from within organizations. Thirdly, and this is more of a personal action, actively avoid discouraging girls for pursuing geeky interests. Boys get rewarded with questions and attention for this behavior. Girls get the opposite signals.

Bilan Liu

Bilan is an applied robotics scientist within the company’s Lab126 organization. She earned her PhD in electrical and computer engineering at the University of Rochester.

Bilan Liu
Bilan Liu

  • The key aspect for a gender equal world is an environment where women share the same opportunities as men, such as quality education.
  • A gender equal world not only calls for the equality of women, but also quality among women. It is beneficial to share the recognition of successful women, as well as to have supportive peers and mentors for young women.
  • We should advocate to elevate women’s voices, both in the workplace and the media. Increasing the representation of women in a workplace not only creates a better workplace, it also changes perceptions about the value that women bring to the table.

Catherine Benoit Norris

Catherine is a science researcher within the company’s Sustainability organization.  She earned her PhD in business administration from the Université du Québec à Montréal.

Catherine Benoit Norris
Catherine Benoit Norris

  • Acknowledge and support workers, students, professionals, and scientists as parents. Until we fully recognize the needs of families, and have a work culture that allows setting limits, women will continue to be held back.
  • Make sure that everyone speaks and are listened to in meetings. Making sure that everybody is being heard and are being paid attention to when they speak is fundamental for a gender-equal global science community.
  • Support, encourage, value, and recognize women academic achievements. Publicly valuing, rewarding, and celebrating competence and achievements in women is a stepping stone towards gender equality in science and beyond.

Tara Taghavi

Tara is a senior applied scientist within the Alexa AI organization. She earned her PhD in computer science from the University of California, Los Angeles.

Tara Taghavi
Tara Taghavi

A first step in promoting gender equality is to involve more women in hiring processes, particularly hiring loops for science roles.

A second step is to facilitate a more favorable work environment for mothers by providing alternate hours, a reduced time schedule, and similar measures so women can grow their careers as they grow their families.

A third step is to empower women to take management roles. Many statistics have been shared regarding the disproportionate number of women who are promoted in comparison to their male counterparts. We should address it by encouraging women to pursue these roles, and then supporting them as they take on the responsibilities of these higher-level roles.

Nedelina Teneva

Nedelina is an applied scientist (search) within the Alexa organization. She earned her PhD in computer science from the University of Chicago.

Nedelina Teneva
Nedelina Teneva

Engaging in cross-disciplinary collaborations forces us to be curious, empowers us to say “I don’t know” and ask others “What do you think?”. 

This helps us better understand others’ lived experiences. In both professional and personal collaborations, we need to apply more rigorously the scientific method, which minimizes the influence of prejudice, by recognizing our biases or pre-existing beliefs and designing appropriate management strategies.

Finally, we need to continue to solidify these processes into platforms and organizations that nurture diverse opinions. Lessons learned from the existing diversity/inclusion efforts within the science community should be utilized in the broader society. 

Nikhita Vedula

Nikhita is an applied scientist with the Alexa Shopping organization. She earned her PhD in computer science and engineering from Ohio State University.

Nikita Vedula
Nikhita Vedula

Education, encouragement, and awareness are key to fostering the growth of a more gender-equal science community.  Throughout my studies — straight through the completion of my PhD — I have seen at best an 80-20 ratio of men to women in classrooms, and academic or industrial positions. This needs to change, and this change needs to begin within our homes.    

Women require support from both men and other women alike, right from their childhood. We need to inspire and motivate women to nurture their dreams, and pursue their unique passions, instead of telling them things like “This field is for men, not for you”.

View from space of a connected network around planet Earth representing the Internet of Things.
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Machine Learning Applied Scientist - Anton van den Hengel
AU, ACT, Canberra
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. 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, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
Machine Learning Applied Scientist - Anton van den Hengel
AU, SA, ADELAIDE
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. 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, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
Applied Scientist
CN, 11, Beijing
Job summaryAmazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems.As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.Your responsibilities include:· Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems.· Analyzing data and metrics relevant to the search experiences.· Working with teams worldwide on global projects.Your benefits include:· Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers· The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact· Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goalsAmazon Search JP负责在Amazon JP购物网站上开发产品搜索功能，并将这些创新扩展到全球。作为这个快速发展中的团队的应用科学家，您将在改善NLP和Amazon产品搜索的体验方面发挥关键作用。我们的最终目标是帮助客户找到他们想要的产品，并发现他们感兴趣的新产品。我们通过开发涵盖多种语言和系统的NLP组件来达成目标。作为Search JP的应用科学家，您将在Amazon网站上设计和实现搜索功能，帮助数百万客户快速找到他们想要的内容。您将基于TB级的产品和流量数据提出NLP和IR领域的创新，构建机器学习模型，并使用离线指标以及A / B测试在线指标进行效果评估，然后将模型集成到面向客户的生产搜索引擎中，从而通过数据，建模，应用，和模型选择完成上线。您的模型同时需要平衡业务指标和毫秒级响应时间的要求。您的职责包括：•设计和实施新功能和机器学习模型，包括应用最先进的深度学习来解决搜索匹配，排名和搜索建议问题。•分析与搜索体验相关的数据和指标。•与全球团队合作开展全球项目。您的收获包括：•通过开发有高度影响力的产品改善数百万客户的体验•有机会使用和创造最新的机器学习方法来解决重要现实问题•作为成长中的团队的一员，我们将共同定义团队的使命，方向以及如何实现目标
Senior Applied Scientist, Amazon Halo
IL, Tel Aviv
Job summaryAmazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, Amazon Echo and Halo.What will you help us create?Key job responsibilitiesYou will be part of a world-class Computer Vision team tasked with solving huge business problems through innovative technology and focus on product industrialization.We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company.Major responsibilities:· Research, design, implement and evaluate novel computer vision algorithms· Work on large-scale datasets, focusing on creating scalable and accurate computer vision systems in versatile application fields· Collaborate closely with team members on developing systems from prototyping to production level· Collaborate with teams spread all over the world· Work closely with software engineering teams to drive scalable, real-time implementations· Track general business activity and provide clear, compelling management reports on a regular basis
Applied Scientist
US, MN, Minneapolis
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
Applied Scientist, AWS AI Labs
US, WA, Bellevue
Job summaryAmazon AI is looking for world class scientists to join its AI Lab. This group is entrusted with developing core machine learning algorithms for AWS. As a part of the AI Lab you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore novel solutions to new problems at scale. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
Applied Scientist III
US, IL, Chicago
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
Applied Scientist
US, TX, Austin
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
Applied Scientist
US, MI, Detroit
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
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