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Credit: Glynis Condon

Seven Amazon scientists shaping the future of AI

To commemorate International Women’s Day, we spoke to women scientists across a variety of research areas at Amazon.

To commemorate International Women’s Day (IWD), during Women's History Month, we asked scientists across a variety of Amazon research areas about their backgrounds, and the most exciting innovations in their fields. Here’s what they had to say.

Xin Luna Dong, principal scientist

Xin Luna Dong
Xin Luna Dong, principal scientist

Dong is a principal scientist, leading the efforts to develop the Amazon Product Knowledge Graph. Dong received her PhD in computer science, with a focus on data integration, from the University of Washington. The personal information management system in Dong’s dissertation (which won the Best Demo award in Sigmod’ 2005), is a personal knowledge graph developed at least five years before the phrase “knowledge graph” was coined. After graduation, Dong led the development of the Knowledge-based Trust project at Google. Dong has co-authored the book Big Data Integration. She is an ACM Distinguished Member, and has received the VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion”. Dong was program committee co-chair for Sigmoid 2018, and is program committee co-chair for VLDB 2020. She also serves on the VLDB endowment and PVLDB advisory committees.

Innovations I find exciting

A recent innovation that I’m most excited about is graph neural network (GNN). Unlike recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which focus much more on regular data such as word sequences, 2-D images, and 3-D videos, GNNs allow us to leverage graphs to capture much more complex relationships. These include elements in the graph, represented by nodes in the graph, and their relationships, represented by edges between the nodes. Examples of graphs influenced by GNNs include social networks, world wide web (WWW) topology, knowledge graphs, and molecular graphs. As we build knowledge graphs for products, it is amazing how many different ways we can benefit from GNNs.

Naturally, we can apply GNNs on the knowledge graphs we have built, to discern interesting patterns to find popular artists in the music domain. We also model webpage layouts as graphs, and model customer behaviors as graphs, so GNNs help us extract relevant knowledge and enrich our knowledge graphs. This new technique enables us to be so much more creative in the practice of constructing knowledge graphs, and applying the findings to real-world applications.

Claire Law, senior technical program manager

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Claire Law, senior technical program manager

Law is senior technical program manager on Amazon’s physical retail team, the team behind the Just Walk Out technology used in Amazon Go and Amazon Go Grocery stores. She studied nanotechnology engineering at the University of Waterloo. Early on, she realized that she didn’t enjoy the type of lab work expected from a researcher in material science. She leveraged a university program, and interned as a software developer, marketer, hardware test engineer, project control officer in consulting, and software program manager. These experiences, coupled with work experiences at Microsoft and Research in Motion, led Law to pursue a career in software.

After a stint in Amazon’s international organization, Law joined the physical retail team to work on machine vision initiatives. On this team, Law is able to leverage her experience in cloud computing and knowledge of optics and photography to build new experiences for physical retail.

Innovations I find exciting

We are only now reaching a level where computer vision can solve real-world problems in a meaningful way. While we still need to be creative in where we look for simplifiers, algorithms are able to solve more and more problems every day. Challenges that looked insurmountable just a couple years ago are now part of production systems across the industry. Checkout-free stores seemed like science fiction before Amazon Go was launched, and now customers are loving this effortless shopping experience in the 25 Amazon Go stores, and the new Amazon Go Grocery store we have open today.

Yoelle Maarek, vice president

Yoelle Maarek, vice president of research and science for Alexa Shopping
Yoelle Maarek, vice president of research and science for Alexa Shopping

Maarek is vice president of research and science, Alexa Shopping. Prior to Amazon, Maarek served in engineering and research leadership roles at Yahoo, Google and IBM. Maarek has been regularly serving as program committee (PC) chair and senior PC committee member at leading academic research conferences related to Web search and data mining, such as SIGIR, The Web Conference, and Web Search and Data Mining (WSDM). She is currently serving on the steering committees of WSDM and the Web Conference series.

She is a member of the Technion Board of Governors and was inducted as an ACM Fellow in 2013. Maarek obtained a PhD in computer science from the Technion, Israel in 1989. She holds an engineering degree from the Ecole des Ponts et Chaussées, and a DEA (graduate degree) in computer science from Paris VI university. Maarek completed her PhD at the Technion in Israel and was a visiting student at Columbia University. She played a pioneering role within industry in researching the field of information retrieval, the computer science discipline behind search, in the pre-Web era, and led the launch of Google Suggest, the query auto-completion capability. As such, she jokingly refers to herself as a “search dinosaur”.

Innovations I find exciting

We are on the verge of making ambient computing happen, and Alexa is pioneering this long-awaited revolution. It forces us to revisit all our assumptions across multiple domains. I see this prevalent especially in search and question answering. These are topics close to my heart. I have been following progress in these areas since I got my PhD thirty years ago. The focus on ambient computing is also a unique opportunity for us at Amazon to demonstrate what we mean by customer-obsessed science. As humans are learning to interact with machines, their behavior is evolving and we need to follow suit. It not only challenges scientists to keep inventing on behalf of customers but also forces all of us to remain humble. We are not here to teach customers how to speak to a machine, but rather to do everything in our power to understand, satisfy and predict their needs so as to constantly wow and delight them.

Angeliki Metallinou, applied science manager

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Angeliki Metallinou, applied science manager

Metallinou is an applied science manager within the Amazon Alexa AI Natural Understanding group. She received both her PhD, and master’s degree in electrical engineering from the University of Southern California. Her interests and experience lie in the areas of spoken and natural language understanding, dialogue systems, machine learning, deep learning, affective computing and applications for education and healthcare.

She has published papers in the areas of speech, language, dialogue, artificial intelligence and multimodal human computer interaction at leading science conferences such as Interspeech, the AAAI Conference on Artificial Intelligence, and the Association for Computational Linguistics (ACL), has served as an area chair for Interspeech 2016, and as a reviewer of papers for several science conferences.

Innovations I find exciting

It is exciting to see how new techniques in deep learning continuously push the boundaries of the state of the art in the fields of dialogue and spoken language processing. I’m very interested in advances around unsupervised, semi-supervised and transfer learning, which allow deep learning models to leverage the power of large corpora without relying on costly and time-consuming manual annotations. Pre-trained language models like BERT and GPT-2 and their use in downstream applications are just a few examples. These innovations are particularly relevant for industry applications where scalability is key.

I am also excited about recent literature in deep learning that is allowing us to develop models to perform complex tasks like higher-level reasoning, for example, over the contents of a document or an image or both, as opposed to simpler classification tasks. I’m also excited to see how these methods can have a positive impact on people through their deployment in products, especially in applications of healthcare, accessibility and education.

Priya Ponnapalli, principal deep learning scientist

Priya Ponnapalli
Priya Ponnapalli, principal deep learning scientist

Ponnapalli is a senior manager and principal deep learning scientist within the Amazon ML Solutions Lab, where she leads a global team of data scientists that help AWS customers accelerate their adoption of ML and cloud technologies across industries, from healthcare and finance to sports. As the leader of Amazon ML Solutions Lab’s sports business, Ponnapalli works with customers including National Football League (NFL), Six Nations Rugby, and Formula 1 (F1), just to name a few, to enhance the fan experience and transform sports using ML.

Ponnapalli is also a senior research affiliate at the Genomic Signal Processing Lab at the University of Utah, and a faculty member at Rutgers Business School, where she teaches ML to business leaders, and works to inspire the next generation of leaders. Prior to joining AWS, she co-founded Eigengene, a data-driven personalized medicine startup and has helped companies like Genentech and Roche establish and build data science teams. For her PhD in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.

Innovations I find exciting

As an Amazon ML Solutions Lab scientist, I’m most excited about real-world applications of ML across industries. I’m interested in innovations to overcome challenges with small, limited datasets that companies often have to contend with. I’m also intrigued by model interpretability and explainability which are key to earning trust and spurring broad adoption. I’m passionate about making ML accessible to all, so it can be used to solve some of the most important problems we are facing, from fighting climate change to treating cancer.

Ana Pinheiro Privette, senior program manager

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Ana Pinheiro Privette, senior program manager

Ana Pinheiro Privette is a senior program manager with Amazon's Sustainability group. She joined the Sustainability Science and Innovation team in September 2017 as the program lead for the Amazon Sustainability Data Initiative (ASDI), a program that seeks to leverage Amazon’s scale, technology, and infrastructure to help create more global innovation for sustainability. ASDI is a Tech-for-Good project and is a joint effort between Amazon Sustainability and the AWS Open Data team focusing on democratizing access to key data and analytical capabilities to anyone working in the sustainability space.

Privette was trained as an environmental engineer and as an earth sciences researcher at the New University of Lisbon (Portugal) and at MIT. She did her doctoral research work at NASA in the Washington D.C. area and as part of her project, she spent a couple of years running scientific field work sites in Africa to support a NASA international field campaign. After spending most of her career at NASA and NOAA as a scientist, Privette led projects for the White House climate portfolio, including the Obama Climate Data Initiative and the Partnership for Resilience and Preparedness (PREP), both focused on delivering better access and use of US Federal climate data to support decision makers.

Innovations I find exciting

As part of ASDI, I work very closely with AWS customers developing applications in the space of sustainability to understand what challenges they may be experiencing and how we may accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. The ASDI currently works with scientific organizations like NOAA, NASA, the UK Met Office and Government of Queensland to identify, host, and deploy key datasets on the AWS Cloud, including weather observations, weather forecasts, climate projection data, satellite imagery, hydrological data, air quality data, and ocean forecast data. These datasets are publicly available to anyone.

In addition, ASDI provides cloud grants to those interested in exploring the use of AWS’ technology and scalable infrastructure to solve big, long-term sustainability challenges with this data. The dual-pronged approach allows sustainability researchers to analyze massive amounts of data in mere minutes, regardless of where they are in the world or how much local storage space or computing capacity they can access.

Nashlie Sephus, manager, applied science

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Nashlie Sephus, applied scientist, Amazon Web Services machine learning team.
Credit: Terrence Wells@PoetWilliamsPhotography

Sephus is an applied scientist on AWS’s artificial intelligence team, focusing on computer vision. In this role, Sephus focuses on the fairness and accuracy of the team’s algorithms. Sephus formerly led the Amazon Visual Search team in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. This technology was a result of former startup Partpic (Atlanta) being acquired by Amazon, for which she was the chief technology officer (CTO). Prior to working at Partpic, she received her PhD in 2014 from the School of Electrical and Computer Engineering at the Georgia Institute of Technology. She received her bachelor’s degree in computer engineering in 2007 from Mississippi State University.

Innovations I find exciting

Since the onset of machine learning and artificial intelligence, neural networks (such as convolutional neural networks (CNNs), and generative adversarial networks (GANs), etc.) and learning algorithms have always excited me. It’s being able to quickly and automatically draw patterns from data, whether it be images, video, or audio at scale, that fascinates me. Since music was my first love (along with karaoke!), music information retrieval has always been a passion of mine. These innovations, when used responsibly and fairly, are able to benefit people in their everyday activities.

Research areas

US, WA, Seattle
Job summaryAt Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday lifeWe are seeking a Data Scientist to be part of the NLU science team for Alexa Shopping. This is a strategic role to shape and deliver our technical strategy in developing and deploying NLU, Machine Learning solutions to our hardest customer facing problems. Our goal is to delight customers by providing a conversational interaction. These initiatives are at the heart of the organization and recognized as the innovations that will allow us to build a differentiated product that exceeds customer expectations. We're a high energy, fast growth business excited to have the opportunity to shape Alexa Shopping NLU is defined for years to come. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Alexa Shopping and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of Language Engineers and Scientists to launch new customer facing features and improve the current features.
US, WA, Bellevue
Job summaryThe People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team.Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions.Key job responsibilitiesUse causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees.A day in the lifeWork with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions.About the teamWe are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, CA, Sunnyvale
Job summaryThe Amazon Alexa app is a companion to Alexa devices for setup, remote control, and enhanced features. The Alexa app understands a customer’s habits, preferences and delivers a personalized experience to help them manage their day by providing relevant information as customers want it. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. As voice-enabled technology becomes increasingly advanced, consumers are demanding more from what their voice products can do. We’re looking for Scientists who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history.As a Data Scientist, you will help build a production scaled personalized recommendation, Machine Learning (ML) and Deep Learning (DL) models to help derive business value and new insights through the adoption of Artificial Intelligence (AI).Key job responsibilitiesThe successful candidate will be responsible for distilling user data insights for ML science applications and influence business decision with data-driven approach to increase Alexa mobile engagement and growth. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions.· Expertise in the areas of data science, machine learning and statistics.· Translate business needs into advanced analytics and machine learning models and provide strong algorithm and coding execution and delivery of Machine Learning & Artificial Intelligence.· Work closely with the engineers to architect and develop the best technical design and approach.· Being able to dive a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to help our customers operationalize models after they are built.A day in the life· Design and review mobile experiments for growth and engagement· Build statistical models and generate data insights to understand mobile growth and retention· Feature engineering to improve ML model performance.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to deploy applications to production· Work with product manager to convert business problems to science problems and define the solutions.About the teamAlexa Mobile Intelligence team is motivated to make Alexa mobile app being the best intelligent assistant and providing personalized relevant features and content by understanding customers' habits, preferences, hence will reach high growth and retention for the app.
US, CA, Sunnyvale
Job summaryOur Alexa Product Advisor (part of Alexa Shopping) vision is to provide the best possible answers for a wide range of questions around product being asked by the customer. Our customers ask various questions to Alexa regarding products, and not all the time we can find an answer in our knowledge sources. "Alexa, how strong is the magsafe on iPhone 12?" is a typical question that could be asked to our system. The first step in providing these answers is to form high quality classification and machine understanding of natural language questions into their core components (shape, product references, attributes, pronouns etc).Alexa Shopping is looking for an experienced Data Scientist to be a part of a team solving complex natural language processing problems and customer demand insights (including segmentation analysis and personas building using big data, ML and potentially AI). This is a blue-sky role that gives you a chance to roll up your sleeves and dive into big data sets in order to build simulations and experimentation systems at scale, build optimization algorithms and leverage cutting-edge technologies across Amazon. This is an opportunity to think big about how to solve a challenging problem for the customers and understand their requirements for products.If you are thinking how big is this, then think how we searched on desktops in 2000's, mobiles in 2010s and on voice and intelligent devices today! We want to provide a great product experience though the intelligence we are building about products on any platform, making it easier for customers to learn about the products on Echo devices, mobile app, desktop, etcYou will work closely with product and technical leaders throughout Alexa Shopping and will be responsible for influencing technical decisions in areas of development/modelling that you identify as critical future product offerings. You will identify both enablers and blockers of adoption for product understanding, and build programs to raise the bar in terms of understanding product questions and predict the shaping of customer utterances as we move from simple to complex utterances.The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ?This is a unique opportunity to get in on the ground floor, shape, and build the next-generation of Amazon Search. We are looking for exceptional scientists and ML engineers who are passionate about innovation and impact, and want to work in a team with a startup culture within a larger organization.Please visit https://www.amazon.science for more information
US, CA, Sunnyvale
Job summaryAmazon Lab 126 specializes in pioneering new home experiences that brings the future one step closer. The most recent invention is Amazon Astro, a home robot that brings the family closer and provides peace of mind. Building a home robot that gracefully moves through an ever-changing environment, such as one’s home, required challenging the state-of-the-art and furthering it, in areas of Perception, SLAM, Mapping and Intelligent Motion to name a few. Packing that technology in an affordable piece of hardware that consistently accomplishes its tasks, is a whole another story!Ada Lovelace, the first computer programmer, once famously said, “Those who have learned to walk on the threshold of the unknown worlds, by means of what are commonly termed par excellence the exact sciences, may then, with the fair white wings of imagination, hope to soar further into the unexplored amidst which we live”. With the launch of Astro, we are on the threshold of something that will change our lives forever. Join us, as we soar further to imagine and invent new experiences that will one day become the future. It is still Day One!Key job responsibilitiesAs a Senior Applied Scientist in Robotics, you will work with a team of smart, passionate and diverse engineers researching and developing mobility solutions for the robot, in the areas of intelligent motion, mapping, exploration - to name a few. You will design solutions for complex and ambiguous problem areas where the business problem or opportunity may not yet be defined. Most business problems that you will take on, require scientific breakthroughs. You will provide context for current technology choices and make recommendations on the right modelling and component design approach to achieve the desired customer experience/business outcome. You will set standards and proactively drive components to utilize and improve on state-of-the-art techniques. Your will create solutions that are inventive, easily maintainable, scalable, and extensible. You will file for patents and publish research work where opportunities arise, and give internal or external presentations about your area of speciality.
IL, Haifa
Job summaryYou: Alexa, I am looking for a role in which I could learn, research, and innovate in AI and, most of all, impact the life of millions of customers worldwide. What do you suggest?Alexa: The Alexa Shopping team is looking for research engineers to help me become the best personal shopping assistant. Do you want to hear more?You: Yes, please!Alexa: As a research engineer, you will work with top researchers and engineers, both locally and abroad, to explore and develop new AI technologies helping me in my journey to become the ultimate shopping assistant for millions of customers around the world. You should have strong computer science foundations, excellent development skills, and some experience with research methodology. You also preferably have some applied or research expertise in at least one of the following fields: Web search and mining, Machine Learning, Natural Language Processing, Computer Vision, Speech Processing, or Artificial Intelligence.
US, CA, Sunnyvale
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 and Amazon Echo. What will you help us create?The Role:We are looking for a passionate, talented and inventive Senior Applied Scientist - Sensors to join our team. As part of the larger technology team working on new consumer technology, your work will have a large impact to hardware, internal software developers, ecosystem, and ultimately the lives of Amazon customers. You must love high quality signal processing, enjoy sensor data analysis, optimizing sensor performance, and have a feel for what a good consumer experience should be like. In this role, you will: - Engage with an experienced cross-disciplinary staff to conceive and design innovative consumer products · Work closely with an internal interdisciplinary team, and outside partners to drive key aspects of product definition, execution and test · Development of new sensor algorithms · Optimization and porting of sensor algorithms to different platforms. · Integrate vendor hardware and software stacks · Be able, and willing, to multi-task and learn new technologies quickly · Be responsive, flexible and able to succeed within an open collaborative peer environment
IE, D, Dublin
Job summary*Flexibility for alternate EU Amazon offices*Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing organization is on the front line of that mission by hiring the hourly fulfilment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon is creating a Workforce Staffing research program.This program will re-invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team will own multi-layered research and program implementation to drive deep learnings, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Are you a tinkerer by trade? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you.As a Manager, Data Science in Workforce Staffing, you will have a strong focus on quantitative data analysis, understanding labor markets and the candidates within them. You will be responsible for building and developing a team, developing roadmaps, and driving business impact through your research at global scale.You will lead data science projects using your deep expertise in statistics (regressions, multilevel models, structural equation models, etc.), and data collection in a variety of settings (e.g., field studies, surveys, existing large data sets) to define and answer nebulous problems. You leverage your quantitative background to develop and test theoretical frameworks and design experiments. You design, deployment, and conduct analysis of our global candidate research activities, using experimental, quasi-experimental, and RCT methods. You relentlessly obsess over understanding our candidates and what attracts them to Amazon. You work with colleagues across Research, Data Science, Business Intelligence and related teams to enable Amazon find and hire the right candidates for the right roles at an unprecedented scale.A customer-obsessed, relentless curiosity is a must, as is commitment to the highest standards of methodological rigor that a given study allows. This role provides opportunity for significant exposure to Amazon’s culture, leadership, and global businesses, and furthermore provides significant opportunity to influence how Workforce Staffing matches talent to business demand.This will be a highly visible role across multiple key deliverables for our global organization. If you are passionate and curious about data, obsess over customers, love questioning the status quo, and want to make the world a better place, let’s chat. #scienceemea
ES, M, Madrid
Job summary*Flexibility for alternate EU Amazon offices*Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing organization is on the front line of that mission by hiring the hourly fulfilment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon is creating a Workforce Staffing research program.This program will re-invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team will own multi-layered research and program implementation to drive deep learnings, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Are you a tinkerer by trade? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you.As a Manager, Data Science in Workforce Staffing, you will have a strong focus on quantitative data analysis, understanding labor markets and the candidates within them. You will be responsible for building and developing a team, developing roadmaps, and driving business impact through your research at global scale.You will lead data science projects using your deep expertise in statistics (regressions, multilevel models, structural equation models, etc.), and data collection in a variety of settings (e.g., field studies, surveys, existing large data sets) to define and answer nebulous problems. You leverage your quantitative background to develop and test theoretical frameworks and design experiments. You design, deployment, and conduct analysis of our global candidate research activities, using experimental, quasi-experimental, and RCT methods. You relentlessly obsess over understanding our candidates and what attracts them to Amazon. You work with colleagues across Research, Data Science, Business Intelligence and related teams to enable Amazon find and hire the right candidates for the right roles at an unprecedented scale.A customer-obsessed, relentless curiosity is a must, as is commitment to the highest standards of methodological rigor that a given study allows. This role provides opportunity for significant exposure to Amazon’s culture, leadership, and global businesses, and furthermore provides significant opportunity to influence how Workforce Staffing matches talent to business demand.This will be a highly visible role across multiple key deliverables for our global organization. If you are passionate and curious about data, obsess over customers, love questioning the status quo, and want to make the world a better place, let’s chat. #scienceemea