Masked Amazon engineer seated at a work table tests a Project Kuiper antenna
Project Kuiper antenna development and testing is occurring at the team's facility in Redmond, Washington.

Nima Mahanfar discusses the science behind Project Kuiper customer terminal antenna

The senior manager of hardware and antenna development for Project Kuiper answers questions related to the development of new custom-built antenna that advances this ambitious project to provide affordable broadband to unserved and underserved communities around the globe.

Amazon today provided an update on Project Kuiper, its initiative to increase broadband access through a constellation of 3,236 low-Earth orbit (LEO) satellites. The team released information on the Ka-band phased array antenna for its low-cost customer terminal, signaling another milestone in the company’s efforts to provide fast and affordable broadband access to communities around the world.

Nima Mahanfar
Nima Mahanfar

The prototype antenna is based on a new architecture designed and developed by the Project Kuiper team. The initial prototype is delivering speeds of up to 400 Mbps, despite a form factor that is approximately 12 inches in diameter and significantly lighter than legacy antenna designs. The reduction in size and complexity will allow Amazon to reduce production costs, contributing to the team’s goal of providing customers a terminal that is affordable and easy to install.

The antenna design and manufacturing effort is led in-house by the Project Kuiper team at Amazon. In advance of today’s announcement, Amazon Science asked Nima Mahanfar, senior manager of antenna development for Project Kuiper, about designing and developing the antenna, the science and engineering challenges his team encountered, tradeoffs the team had to wrestle with, and more.

The Kuiper prototype antenna is smaller and lighter than legacy Ka-band antennas. How did your team achieve that goal? 

The key advancement was combining transmit and receive phased-array antennas into one aperture. This can be done in other frequency bands, but Project Kuiper plans to operate in Ka-Band, which has transmit and receive frequencies that are much further apart from one another. This makes it difficult, nearly impossible in fact, to combine transmit and receive into one aperture. Phased arrays are a class of radiating system, where multiple antennas — it could be two, it could be thousands— are on the same aperture, creating a focused beam of radio waves. The distance between the antennas — or the relation between these antennas — is decided by the frequency. If the frequencies are close to each other, as with Ku-Band, you can combine the transmit and receive function into one and it works. When the frequencies are far apart, as with Ka-Band, it’s much more difficult to utilize the same lattice for both. This has never been done before — until now. 

Our design involves hundreds of antennas in each aperture, with receive antennas operating at 18 to 20 gigahertz (GHz) and transmit antennas operating at 28 to 30 GHz. Our breakthrough came from the realization that we could get to a single lattice by looking at each antenna element uniquely — helping reduce the size and cost of our entire terminal.

What were the science and engineering tradeoffs your team had to wrestle with in developing this new architecture, and what tenets guided your team’s work?

There were several, but I’ll focus on two key ones.

One is balancing the tradeoff between the transmit and receive functionality. From a customer obsession perspective, we realized that what affects the customer most is the receive function. In other words, we typically receive more information than we transmit. So we always err on the side of improving receive performance. On the transmit side, if you compromise performance, you can always increase the transmitted power by a little.

Project Kuiper customer terminal animation
"The key advancement was combining transmit and receive phased-array antennas into one aperture," Nima Mahanfar, senior manager of antenna development for Project Kuiper said. "Our breakthrough came from the realization that we could get to a single lattice by looking at each antenna element uniquely – helping reduce the size and cost of our entire terminal."

The second tradeoff relates to how easy our antenna is to manufacture. If our design was overly complex, it couldn’t be built affordably, or it couldn’t be scaled in production. We had to ask ourselves, at what point does this combined aperture become twice as complex as the single aperture, and would it still make sense? Our objective was to ensure our antenna was mass producible by mainstream circuit board manufacturers, allowing us to take advantage of economies of scale and produce millions at low cost. We had to keep our design as simple as possible to satisfy this objective, and this is an area where collaboration between scientists and manufacturing and hardware engineers was so important.

You received your PhD in high-frequency electronics and microwaves from the Université de Limoges and have been working in this field for a couple of decades. What are some of the key advancements that make Project Kuiper viable today? What are some of the interesting science and engineering challenges still left to address?

There are several important trends that make a project like this possible, and interestingly, few of them have to do with aerospace and satellite technology.

One is silicon. CMOS [complementary metal oxide semiconductor] technology has improved significantly, is available at a low cost, and can operate at higher and higher frequencies. Components that were once a luxury and confined primarily to the space and military industries, now are readily available and cost just a few dollars, or even cents. That has opened up this whole area for exploration.

The second trend comes from cellular technology and the cloud. As adoption and demand for these technologies has increased, we’ve seen higher frequency materials, and components available at larger scale and lower cost.  At one time, building a printed circuit board at 30GHz was a niche thing.  There were only a couple of manufacturers in the United States who could do this. They were expensive and they didn’t scale. Right now with 5G and even 4G cellular technology, basically we have more and more mass production of radio frequency (RF) at scale and at very high frequencies and customers are benefitting from this. 

So those are the two technology trends that are helping us innovate easier, better and cheaper. The whole area of RF used to be exclusive to the space industry, and the military.  That’s not the case anymore, and that’s great news for engineers like myself.

As for the challenges left to solve, I feel we can make phased array technology even more affordable. Not just by buying cheaper materials, but by developing new technologies and architectures that could be fundamentally different from today’s approach. We should be open-minded about the possibilities, and we are pursuing many of them already.

Another relates to the phased array technology we are building for our satellites in space, where the challenges are a little different. Cost is still important, but more importantly, we want to reduce watts per gigabits per second. Solving power challenges in space is hard, and dissipating the heat from that power is even harder. There’s no air to cool it. So having a low-power system that can provide many gigabytes of service to customers is key. How can we reduce the power consumption of these space-borne phased arrays? That’s one of the other big challenges facing anyone deploying phased array antennas in low earth orbit.

What type of scientist and engineer are you seeking to help address those challenges?

We’re seeking individuals with strong, fundamental science skills who understand the physical limits of what’s possible, and can deliver performance to that physical limit, or who can explain the imperfections for why we can’t. We want to deliver the best possible performance at the lowest possible price for our customers. To do this, we need to understand what possible based on physics rather than an arbitrary limit based on what’s been achieved in legacy systems. 

Project Kuiper is made up of engineers and scientists with decades of experience in satellite communications, many of whom hold multiple patents for their work. Most of the folks I work with are PhDs, many in the field of electromagnetics, who aren’t fearful of doing original work. This is particularly germane for phased array technology where we’ve shifted to using printed circuit board technology versus expensive ceramics and other materials. So we’re seeking individuals who can recognize science projects from the real thing, and can then build advanced designs that come close to the physical limits of what’s possible.  If this resonates with any of your readers, I hope they will visit our jobs page.

View from space of a connected network around planet Earth representing the Internet of Things.
Sign up for our newsletter

Related content

US, CA, Santa Clara
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive 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.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.
US, NY, New York
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive 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.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.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. 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. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. 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. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, WA, Seattle
Job summaryHow can we create a rich, data-driven shopping experience on Amazon? How do we build data models that helps us innovate different ways to enhance customer experience? How do we combine the world's greatest online shopping dataset with Amazon's computing power to create models that deeply understand our customers? Recommendations at Amazon is a way to help customers discover products. Our team's stated mission is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". We strive to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.Key job responsibilitiesScaling state of the art techniques to Amazon-scaleWorking independently and collaborating with SDEs to deploy models to productionDeveloping long-term roadmaps for the team's scientific agendaDesigning experiments to measure business impact of the team's effortsMentoring scientists in the departmentContributing back to the machine learning science community
US, NY, New York
Job summaryAmazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Inclusive Team Culture Here 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 Balance Our 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 Growth Our 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. We care about your career growth and strive to assign projects based on what will help each team member develop and enable them to take on more complex tasks in the future.A day in the lifeAbout the hiring groupJob responsibilities* Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.* Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.* Report results in a scientifically rigorous way.* Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.
US, MA, Westborough
Job summaryAre 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 who work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences. 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.Amazon.com empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas.This role is a 6-month Co-Op to join AR full-time (40 hours/week) from January 9, 2023 to June 23, 2023. Amazon Robotics co-op opportunity will be Hybrid (2-3 days onsite) and based out of the Greater Boston Area in our two state-of-the-art facilities in Westborough, MA and North Reading, MA. Both campuses provide a unique opportunity to have direct access to robotics testing labs and manufacturing facilities.Key job responsibilitiesWe are seeking data scientist co-ops to help us analyze data, quantify uncertainty, and build machine learning models to make quick prediction.
US, WA, Seattle
Job summaryDo you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.Major responsibilities Use statistical and machine learning techniques to create scalable risk management systemsLearning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trendsDesign, development and evaluation of highly innovative models for risk managementWorking closely with software engineering teams to drive real-time model implementations and new feature creationsWorking closely with operations staff to optimize risk management operations,Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationTracking general business activity and providing clear, compelling management reporting on a regular basisResearch and implement novel machine learning and statistical approaches
US, CA, Palo Alto
Job summaryAmazon is investing heavily in building a customer centric, world class advertising business across its many unique audio, video, and display surfaces. We are looking for an Applied Scientist who has a deep passion for building machine-learning solutions in our advertising decision system. In this role, you will be on the cutting edge of developing monetization solutions for Live TV, Connected TV and streaming Audio. These are nascent, high growth areas, where advertising monetization is an important, fully integrated part of the core strategy for each business.Key job responsibilitiesRapidly design, prototype and test machine learning algorithms for optimizing advertising reach, frequency and return on advertising spendBuild systems that extract and process volumes of disparate data using a variety of econometric and machine learning approaches. These systems should be designed to scale with exponential growth in data and run continuously.Leverage knowledge of advanced software system and algorithm development to build our measurement and optimization engine.Contribute intellectual property through patent generation.Functionally decompose complex problems into simple, straight-forward solutions.Understand system inter-dependencies and limitations as well as analytic inter-dependencies to build efficient solutions.A day in the lifeAs an Applied Scientist, you will be tasked with leading innovations in machine learning algorithms to deliver ads across platforms influencing product features and architectural choices for decision making systems. You will need to work with data scientists to invent elegant metrics and associated measurement models, and develop algorithms that help advertisers test and learn the impact of advertising strategies across channels on these metrics while ensuring a great customer experience.
US, WA, Seattle
Job summaryThe Amazon Devices Demand Science team is looking for an energetic, focused and skilled, truly innovative and technically strong research scientist with a background in data analytics, machine learning, data science, decision science and statistical modeling/analysis to help with demand forecasting and planning for the entire Amazon device family of products, services and accessories.Amazon is looking for a talented Senior Research Scientist to join the Amazon Devices team. We materially impact Amazon’s device businesses by forecasting demand, influencing promotion pricing and identifying optimal inventory allocation of all Amazon Devices using ML, operations research and big data.Key job responsibilitiesIn this role, you will have an opportunity to both develop advanced scientific solutions and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data and ML models, building prototypes and exploring conceptually new solutions, to working with partner teams for prod deployment. You will collaborate closely with scientists, engineering peers as well as business stakeholders. You will be responsible for researching, prototyping, experimenting, analyzing predictive models and developing artificial intelligence-enabled automation solutions.As a Senior Research Scientist, you will:• research and develop new methodologies for demand forecasting, alarms, alerts and automation.• apply your advanced data analytics, machine learning skills to solve complex demand planning and allocation problems.• work closely with stakeholders and translate data-driven findings into actionable insights.• improve upon existing methodologies by adding new data sources and implementing model enhancements.• create and track accuracy and performance metrics.• create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.• drive best practices on the team; mentor and guide junior members to achieve their career growth potential.A day in the lifeThis role will be a Problem Solver, Doer, Detail Oriented, Communicator and Influencer.Problem Solver: Ability to utilize exceptional modeling and problem-solving skills to work through different challenges in ambiguous situations.Doer: You’ve successfully delivered end-to-end operations research projects, working through conflicting viewpoints and data limitations.Detail Oriented: You have an enviable level of attention to details.Communicator: Ability to communicate analytical results to senior leaders, and peers.Influencer: Innovative scientist with the ability to identify opportunities and develop novel modeling approaches in a fast-paced and ever-changing environment, and gain support with data and storytelling.About the teamWe are a growing team continues to operate in "startup" mode to prove new business ideas, while strengthening our core ML platforms.This role is available for the following locations: Seattle/Bellevue, Washington; Arlington, Virginia (HQ2); Denver, Colorado; Bay Area/Los Angeles Metro, California; and Nashville, Tennessee. (other US Locations can be discussed further)