Amazon Halo Rise advances the future of sleep

Built-in radar technology, deep domain adaptation for sleep stage classification, and low-latency incremental sleep tracking enable Halo Rise to deliver a seamless, no-contact way to help customers improve sleep.

The benefits of quality sleep are well documented, and sleep affects nearly every aspect of our physical and emotional well-being. Yet one in three adults doesn’t get enough sleep. Given Amazon’s expertise in machine learning and radar technology innovation, we wanted to invent a device that would help customers improve their sleep by looking holistically at the factors that contribute to a good night’s rest.

That’s why we’re excited to announce that Amazon has unveiled its first dedicated sleep device — Halo Rise, a combined bedside sleep tracker, wake-up light, and smart alarm. Powered by custom machine learning algorithms and a suite of built-in sensors, Halo Rise accurately determines users’ sleep stages and provides valuable insights that can be used to optimize their sleep, including information about their sleep environments. Halo Rise has no sensors to wear, batteries to charge, or apps to open. And since a good wake-up experience is core to good sleep, Halo Rise features a wake-up light and smart alarm, designed to help customers start the day feeling rested and alert.

Halo Rise in action
A built-in radar sensor uses ultralow-power radio signals to sense respiration and movement patterns and determine sleep stages.

Designing with customer trust as our foundation

Customer privacy and safety are foundational to Halo Rise, and that's evident in both the hardware design and the technologies used to power the experience. Halo Rise features neither a camera nor a microphone and instead relies on ambient radar technology and machine learning to accurately determine sleep stages: deep, light, REM (rapid eye movement), and awake.

The technology at the core of Halo Rise is a built-in radar sensor that safely emits and receives an ultralow-power radio signal. The sensor uses phase differences between reflected signals at different antennas to measure movement and distance. Through on-chip signal processing, Halo Rise produces a discrete waveform corresponding to the user’s respiration. The device cannot detect noise or visual identifiers associated with an individual user, such as body images.

Using built-in radar technology enables us to prioritize customer privacy while still delivering accurate measurements and useful results. Customers have the option to manually put Halo Rise into Standby mode, which turns off the device’s ability to detect someone’s presence or track sleep.

Halo Rise hardware design
Halo Rise features a suite of sensors to accurately track your sleep and measure your room’s temperature, humidity, and light levels. 

Intuitive and accurate experience

To design the sleep-tracking algorithm that powers Halo Rise, we thought about the most common bedtime behaviors and the ways in which customers and their families (pets included) might engage with the bedroom. This led us to innovate on five main technological fronts:

  • Presence detection: Halo Rise activates its sleep detection only when someone is in range of the sensor. Otherwise, the device remains in a monitoring mode, where no data is transmitted to the cloud.
  • Primary-user tracking: Halo Rise distinguishes the sleep of the primary user (the user closest to the device) from that of other people or pets in the same bed, even though the respiration signal cannot be associated with individual users.
  • Sleep intent detection: Halo Rise detects when the user first starts trying to sleep and distinguishes that attempt from other in-bed activities — such as reading or watching TV — to accurately measure the time it takes to fall asleep, an important indicator of sleep health.
  • Sleep stage classification: Halo Rise reliably correlates respiration-driven movement signals with sleep stages.
  • Smart-alarm integration: During the user’s alarm window, the Halo Rise smart alarm checks the user’s sleep stage every few minutes to detect light sleep, while also maximizing sleep duration.
Halo-Vienna-MM_Wave-Chart.png
A combination of breathing and movement patterns enables Halo Rise to determine the primary user for the sleep session and to measure that person’s sleep throughout the night.

Presence detection

Halo Rise has an easy setup process. To get started, a customer will place Halo Rise on their bedside table facing their chest and note in the Amazon Halo app what side of the bed they sleep on — and that’s it: Halo Rise is ready to go. The radar sensor detects motion within a 3-D geometric volume that fans out from the sensor, an area called the detection zone. Within this zone, the presence detection algorithm estimates the location of the bed and an “out-of-bed” area between the bed and the device.

On-chip algorithms detect the motion and location of respiration events within the detection zone. In both cases — motion and respiration — the algorithm evaluates the quality of the signals. On that basis, it computes a score indicating its confidence that the readings are reliable and a user is present. Only if the confidence score crosses a reliability threshold does Halo Rise begin streaming sensor data to the cloud, where it is processed by the primary-user-tracking algorithm.

Radar Fan.png
The Halo Rise detection zone is the region within which the radar sensor senses motion and location.

Primary-user tracking

We know that many of our customers share their beds, be it with other people or with pets, so our algorithms are designed to track the sleep of only the primary user. Halo Rise starts a sleep session after it detects someone’s presence within the detection zone for longer than five minutes. From there, the primary-user-tracking algorithm runs continuously in the background, sensing the closest user’s sleep stages. As long as the user sleeps on their side of the bed, and their partner sleeps on the other side, Halo Rise will track the primary user’s sleep quality irrespective of who comes to bed first and who leaves the bed last.

During the sleep session, Halo Rise dynamically monitors changes in the user’s distance from the sensor, the respiration signal quality, and abrupt changes in respiration patterns that indicate another person’s presence. These changes cause the algorithm to reassess whether it’s actually sensing the intended user and to ignore the data unrelated to the primary user. For instance, if the user gets into bed after their partner has already fallen asleep, or if they use the restroom in the middle of the night, Halo Rise detects that and adjusts the sleep results accordingly.

Sleep intent detection

Another big algorithmic challenge we faced was determining when a user is quietly sitting in bed reading their Kindle or watching TV rather than trying to fall asleep. The time it takes to fall asleep (also known as sleep latency) is an important indicator of sleep health. Too short of a time may result from sleep deprivation, while too long of a time may be due to difficulty winding down.

To address this problem, we used a combination of presence and primary-user tracking along with a machine-learning model trained and evaluated on tens of thousands of hours of sleep diaries to accurately identify when the user is trying to sleep. The model uses sensor data streamed from the device — including respiration, movement, and distance — to generate a sleep intent score. The score is then post-processed by a regularized change-point detection algorithm to determine when the user is trying to fall asleep or wake up.

Halo Rise Sleep Intent v2.png
A machine learning model trained on thousands of hours of sleep uses respiration, movement, and distance data to generate a sleep intent score.

Sleep stage classification

Wearable health trackers like Halo Band and Halo View use heart rate and motion signals to determine sleep stages during the night, but Halo Rise uses respiration. To learn how to reliably recognize those stages, we needed to develop new machine learning models.

We pretrained a deep-learning model to predict sleep stages using a rich and diverse clinical dataset that included tens of thousands of hours of sleep collected by academic and research sources. The research included sleep data measured using the clinical gold standard, polysomnography (PSG). PSG studies use a large array of sensors attached to the body to measure sleep, including respiratory inductance plethysmography (RIP) sensors, whose output is analogous to the respiration data measured by Halo Rise.

Pretraining the model to predict sleep stages from RIP sensors enabled it to develop meaningful representations of the relationship between respiration and sleep prior to additional training on radar datasets collected alongside PSG. To collect radar training data for the models, we partnered with sleep clinics to conduct thousands of hours of PSG studies. Ultimately, this enables our models to classify sleep stages using just a built-in radar in the comfort of a customer’s home.

Halo_hypnogram.png
In the morning, customers can access a sleep hypnogram that provides a detailed breakdown of time spent in each sleep stage throughout the night.

A smarter wake-up experience

When woken naturally during a light sleep stage, people are most likely to feel rested, refreshed, and ready to tackle the day. Consequently, Halo Rise features a wake-up light, which gently simulates the colors and gradual brightening of a sunrise, and a smart alarm. Customers can also set an audible smart alarm that’s integrated with our sleep stage classification algorithms, optimizing their wake experience. Ahead of their scheduled wake-up time, the audible smart alarm monitors their sleep stages and wakes them up at their ideal time for getting up. This combination of wake-up light and smart alarm is shown to increase cognitive and physical performance throughout the day.

The smart-alarm algorithms are trained around two factors: sensing when the user is in light sleep and maximizing the user’s sleep duration. For the first component, Halo Rise needs to continuously monitor sleep stages during the alarm window — the 30 minutes before a user’s scheduled alarm — to identify when the user has entered a light sleep stage, known as the “wake window.”

At this phase, our algorithms work to sense “wakeable events,” such as a change in motion or breathing. This requires incrementally computing sleep stages to trigger the alarm with low latency. Unlike many sleep algorithms, Halo Rise does not require data from the entirety of the sleep session to classify sleep stages, allowing predictions to be used directly for alarm triggers as data is streamed.

For the second component, the system’s models are trained to predict the latest moment to trigger the alarm during the wake window. This ensures that as the user drifts between sleep stages, they are getting those crucial minutes of additional sleep before the alarm goes off.

The Halo Rise wake-up light
Halo Rise identifies a “wake window” when the user is in light sleep, while also maximizing sleep duration before activating an audible smart alarm.

A solution you can trust

To evaluate our machine learning algorithms, we collected thousands of hours of sleep studies comparing Halo Rise to PSG for over a hundred sleepers, developed with input from leading sleep labs. While sleep studies are typically conducted in sleep labs, we performed in-home PSG studies at participants’ homes under supervision of registered PSG technologists to test the device in naturalistic settings.

We used three different registered PSG technologists to reliably annotate ground truth sleep stages per the American Academy of Sleep Medicine’s scoring rules. We then compared Halo Rise’s outputs to the ground truth sleep data across 14 different sleep metrics — including time asleep, time awake, time to fall asleep, and accuracy for every 30 seconds — following analysis guidelines from a standardized framework for sleep stage classification assessment. This evaluation was supplemented by thousands of sleep diaries from our beta trials, expanding our evaluation to a diverse population of adults to account for variations in preferred sleep postures, age, body shapes, and other background conditions.

What’s next?

As we look to invent new products that help our customers live better longer, Halo Rise is an important step in giving our customers greater agency over their health and well-being. By looking holistically at the end-to-end sleep experience — not just going to sleep but also getting up in the morning — Halo Rise unlocks an entirely new way for customers to understand and manage sleep. We’re excited to help them make sense of valuable sleep data, from the quality and quantity of their sleep to their room’s environment, and deliver actionable insights and resources to improve it in the future. Halo Rise is just getting started, and we are going to learn from our customers how this technology can continue to evolve and become even more personalized to better meet their needs.

Research areas

Related content

US, CA, Santa Clara
Machine 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 Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an 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 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. We’re looking for talented scientists capable of applying 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. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML 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 Generative AI solutions to solve them Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solutions About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: San Francisco, CA, USA | Santa Clara, CA, USA
US, NY, New York
We are looking for a motivated and experienced Senior Data Scientist with experience in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture with deep understanding in advertising businesses, to be part of a team of talented scientists and engineers to innovate, iterate, and solve real world problem with cutting-edge AWS technologies. In this role, you will take a leading role in defining the problem, innovating the ML/AI solutions, and information the tech roadmap. You will join a cross-functional, fun-loving team, working closely with scientists and engineers in a daily basis. You will innovate on behalf of our customers by prototyping, delivering functional proofs of concept (POCs), and partnering with our engineers to productize and scale successful POCs. If you are passionate about creating the future, come join us as we have fun, and make history. Key job responsibilities - Define and execute a research & development roadmap that drives data-informed decision making for marketers and advertisers - Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions - Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale - Partner with product managers and stakeholders to define forward-looking product visions and prospective business use cases - Drive and lead of culture of data-driven innovations within and outside across Amazon Ads Marketing orgs About the team Marketing Decision Science provides science products to enable Amazon Ads Marketing to deliver relevant and compelling guidance across marketing channels to prospective and active advertisers for success on Amazon. We own the product, technology and deployment roadmap for AI- and analytics-powered products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right team to help them make the data-informed advertising decisions. Our science-based products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Seattle
Are you excited about developing models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a collaborative Applied Scientist to focus on computer vision machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
Looking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities The Senior Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy. About the team NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. We are open to hiring candidates to work out of one of the following locations: London, GBR
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. We are open to hiring candidates to work out of one of the following locations: London, GBR