Amazon Research Award recipient Shrikanth Narayanan is on a mission to make inclusive human-AI conversational experiences.
Amazon Research Award recipient Shrikanth Narayanan, university professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, is on a mission to make inclusive human-AI conversational experiences.
USC

“Who we are shapes what we say and how we say it”

Amazon Research Award recipient Shrikanth Narayanan is on a mission to make inclusive human-AI conversational experiences.

To hear Shrikanth Narayanan describe it, every single human conversation is a feat of engineering — a complex system for creating and interpreting a dizzying array of signals.

“When I'm speaking, I'm producing this audio signal, which you're able to make sense out of by processing it in your auditory system and neural systems,” Narayanan says. “Meanwhile, you’re decoding my intent and emotions. I've always been fascinated by that.”

Narayanan uses signal processing and machine learning to better understand this sort of real-world information transfer as university professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC).

In 2020, his lab earned an Amazon Research Award for work on creating “inclusive human-AI conversational experiences for children." Today, he continues to collaborate with Amazon researchers through The Center for Secure and Trusted Machine Learning at the USC Viterbi School of Engineering. He’s also gained a reputation for training future Amazon scientists, with dozens of his former students now working full time for the company.

They’re finding new approaches to machine learning privacy, security, and trustworthiness that are helping to shape a future that Narayanan hopes will be more equitable, more secure, and more empathetic.

A signal with ‘complex underpinnings’

Narayanan recalls being fascinated by the scientific side of the human experience as early as high school. At the time, he says, he was mainly interested in our physiology. But in retrospect, he says, his curiosity had the tenor of a tinkering engineer.

Related content
With little training data and no mapping of speech to phonemes, Amazon researchers used voice conversion to generate Irish-accented training data in Alexa’s own voice.

“I was always interested in how it all worked,” he says. “I wanted to know how the heart worked, what happened in the brain, how it worked together. I was looking at humans through this lens of systems — the information flow that happens within individuals and between individuals.”

It was in the early ‘90s, while he was pursuing a PhD in electrical engineering at the University of California, Los Angeles, that he managed to combine his diverse interests.
“I was training in electrical engineering, but I really wanted the chance to look at something more directly connected to those human systems,” he says. He got the chance to intern at AT&T Bell Laboratories and realized human language held all the sorts of mysteries he’d been hoping to help solve.

Related content
Alexa Fund company unlocks voice-based computing for people who have trouble using their voices.

“Human speech is a signal that has these complex underpinnings,” he says. “There’s a cognitive aspect, the mind, and motoric aspects. We use the vocal instrument to create the signal, which in turn gets processed by people.”

Narayanan was fascinated by all the data involved in helping a conversation go right — and how easily conversations can go wrong.

He also became interested in the ways developmental disorders and health conditions could change the process of creating and interpreting speech, as well as how the rich diversity of human cultural contexts could impact the efficacy of voice recognition and synthesis.

In 2000, Narayanan founded USC’s Signal Analysis and Interpretation Laboratory (SAIL) to focus “on human-centered signal and information processing that address key societal needs.”

Over the last two decades, SAIL has enabled advances in audio, speech, language, image, video and bio signal processing, human and environment sensing and imaging, and human-centered machine learning. The lab also applies their findings to create “technologies that are inclusive, and technologies that support inclusion,” Narayanan says.

Related content
In a top-3% paper at ICASSP, Amazon researchers adapt graph-based label propagation to improve speech recognition on underrepresented pronunciations.

By that, he means that in addition to making sure technologies like voice recognition actually work for everyone — some of his earliest work involved helping AI pick up on a speaker’s emotional state regardless of their spoken language — he uses signal analysis and interpretation to help uncover and spotlight inequality.

In 2017, SAIL created algorithms for analyzing movie script dialogue in order to measure representation of BIPOC characters. Another SAIL tool analyzed footage directly to track and tally female screen time and speaking time.

In 2019, the lab reported that an algorithm trained on human speech patterns could predict whether or not couples facing hard times would actually stay together. It did so even better than a trained therapist presented with video recordings of the couples in question. Instead of interpreting the content of the discussions —or any visual cues— the algorithm focused on factors like cadence and pitch. A similar tool predicted changes in mental well-being in psychiatric patients as well as human physicians could.

Building trust in AI

“Even if we speak the same language,” Narayanan says, “who we are shapes what we say and how we say it. And this is particularly fascinating for children, because their speech represents a moving target with ongoing developmental changes.”

Even if we speak the same language, who we are shapes what we say and how we say it. And this is particularly fascinating for children, because their speech represents a moving target with ongoing developmental changes.
Shrikanth Narayanan

It’s not just that a child’s vocal instrument is constantly changing as they grow. They’re also developing cognitively and socially. That can mean rapid shifts in the words they use and how they use them. When you add in other factors that might make those speech shifts different from the already diverse average —cultural contexts, speaking or hearing impairments, cognitive differences, or developmental delays — training a voice assistant to effectively communicate with kids poses a real challenge.

The analysis gets even more complicated when interacting with two humans at once, especially if one is an adult and one is a child. Using Amazon Elastic Compute Cloud (Amazon EC2) to process their data, SAIL made advances in core competences like automatic speech recognition to improve speaker diarization — the process of partitioning audio of human speech to determine which person is speaking when.

Related content
Alexa Fund company’s assisted reality tech could unlock speech for hundreds of millions of people who struggle to communicate.

In 2021, SAIL also published a detailed empirical study of children’s speech recognition. They found that the state-of-the-art end-to-end systems setting high benchmarks on adult speech had serious shortcomings when it came to understanding children. The following year, the lab proposed a novel technique for estimating a child’s age based on temporal variability in their speech.

By measuring the same aspects of speech that make children difficult for AI to interact with — like variations in pause length and the time it takes to pronounce certain sounds — his team was able to reliably measure a child’s developmental stage. That could help AI adapt to the needs of users with less sophisticated language skills. Because the analysis relies on signals that can be stripped of other identifying information, the method also has the potential to help protect a child’s privacy.

Narayanan refers to this and similar projects as “trustworthy speech processing,” and says he and collaborators he’s found through Amazon are working to spread interest in the idea across their booming field. In March, the International Speech Communication Association (ISCA) awarded him their ISCA Medal for Scientific Achievement — the group’s most prestigious award — for his sustained and diverse contributions to speech communication science and technology and its application to human-centered engineering systems. He will receive the medal and deliver the opening keynote lecture in August at Interspeech 2023, held in Dublin, Ireland.

Narayanan notes that the last five years have seen radical changes in our ability to gather and analyze information about human behavior.

Related content
Generative AI raises new challenges in defining, measuring, and mitigating concerns about fairness, toxicity, and intellectual property, among other things. But work has started on the solutions.

“The technology systems have made this sort of engineering leap and allowed applications we hadn’t even imagined yet,” he says. “All these people are interacting with these devices in open, real-world environments, and we have the machine learning and deep learning advances to actually use that audio data.”

The next big challenge, he says, is figuring out how to process that data in a way that not only serves the user, but ensures their trust. In addition to continuing to study how various developmental differences might impact voice recognition—and how AI can learn to adapt to them—Narayanan hopes to find new ways to mask as much user data as possible for privacy while pulling out the signals that voice assistants need.

Ushering in the next generation of researchers

Working with Amazon enables Narayanan’s lab to explore key research themes through a practical lens. He notes that collaborations of this nature provide academics like himself with the time and support to tackle complex, delicate research questions — such as those involving children and other vulnerable populations.

In addition, Naraynan’s graduate students get to work directly with Amazon scientists to understand the potential practical applications of their research.

“This kind of partnership really takes research to the next level,” he says.

The AI revolution that's happening has a very nice connection to what's happening at Amazon, so naturally it was a place where my students found the most exciting challenges and opportunities.
Shrikanth Narayanan

Narayanan has also encouraged dozens of his students to pursue internships at Amazon to explore what industry has to offer. Just as his time at Bell Laboratories helped to crystalize his own interests, he says, he’s watched countless young engineers find exciting new applications for their skills at Amazon.

What started as a gentle nudge to consider Amazon internships and job postings has grown into a steady pipeline of Amazon hires — one that Narayanan says owes entirely to the merits of his lab’s alums.

Angeliki Metallinou, a senior applied science manager for Alexa AI, joined Amazon fulltime in 2014 with Narayanan’s encouragement. Alexa was a top-secret project at the time, so she didn’t know exactly what she’d be working on until she got there. She credits Narayanan with encouraging her to dive in.

Related content
How he parlayed an internship to land an expanded role at Amazon while pursuing his master’s degree.

“As a student, I hadn’t realized the extent that Amazon scientists collaborate with academia and are able to publish their work at top tier venues and conferences,” she recalls. “I wasn’t even aware that there was such a strong science community here. But Shri already had a few former PhD students working at Amazon, and he recommended it as a great place for an industry career.”

Rahul Gupta, a senior applied scientist for Amazon Alexa, first connected with Amazon for an internship near the end of his SAIL PhD in 2015. These days, he says, he has one or two SAIL students doing summer internships in his group alone.

“There's really good cultural alignment between SAIL and Amazon,” Gupta says.

Narayanan, who proudly displays photos of all of his lab graduates on the wall of his office, admits he’s lost count of how many have worked at Amazon over the years.

“It's exciting,” he says. “The AI revolution that's happening has a very nice connection to what's happening at Amazon, so naturally it was a place where my students found the most exciting challenges and opportunities. But I’ve also seen many of them progress into leadership positions, which I did my best to set them up for — I always encourage creativity and collaboration, and I don’t micromanage them in my lab.”

Now that his graduates are thriving at Amazon, he says, the internship opportunities for his current students are all the more robust.

“It sustains itself,” he says. “They shine in what they do at Amazon and in the community, and that connects back to the lab. It’s incredibly exciting.”

Related content

US, WA, Bellevue
The Routing and Planning organization supports all parcel and grocery delivery programs across Amazon Delivery. All these programs have different characteristics and require a large number of decision support systems to operate at scale. As part of Routing and Planning organization, you’ll partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting portfolio of research areas including network optimization, routing, routing inputs, electric vehicles, delivery speed, capacity planning, geospatial planning and dispatch solutions for different last mile programs leveraging the latest OR, ML, and Generative AI methods, at a global scale. We are actively looking to hire senior scientists to lead one or more of these problem spaces. Successful candidates will have a deep knowledge of Operations Research and Machine Learning methods, experience in applying these methods to large-scale business problems, the ability to map models into production-worthy code in Python or Java, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big research challenges. Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. 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 reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Mentorship & Career Growth We care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. Key job responsibilities • Invent and design new solutions for scientifically-complex problem areas and identify opportunities for invention in existing or new business initiatives. • Successfully deliver large or critical solutions to complex problems in the support of medium-to-large business goals. • Influence the design of scientifically-complex software solutions or systems, for which you personally write significant parts of the critical scientific novelty. • Apply mathematical optimization techniques and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. • Research, prototype, simulate, and experiment with these models and participate in the production level deployment in Python or Java. • Make insightful contributions to teams's roadmaps, goals, priorities, and approach. • Actively engage with the internal and external scientific communities by publishing scientific articles and participating in research conferences.
US, WA, Seattle
The Creator team’s mission is to make Amazon the Earth’s most desired destination for commerce creators and their content. We own the Associates and Influencer programs and brands across Amazon to ensure a cohesive experience, expand creators’ opportunities to earn through innovation, and launch experiences that reinforce feelings of achievement for creators. Within Creators, our Shoppable Content team focuses on enriching the Amazon shopping experience with inspiring and engaging content like shoppable videos that guides customers’ purchasing decisions, building products and services that enable creators to publish and manage shoppable content posts internal teams to build innovative, content-first experiences for customers. We’re seeking a customer-obsessed, data driven leader to manage our Science and Analytics teams. You will lead a team of Applied Scientists, Economists, Data Scientists, Business Intelligence Engineers, and Data Engineers to develop innovative solutions that help us address creator needs and make creators more successful on Amazon. You will define the strategic vision for how to make science foundational to everything we do, including leading the development of data models and analysis tools to represent the ground truth about creator measurement and test results to facilitate important business decisions, both off and on Amazon. Domains include creator incrementality, compensation, acquisition, recommendations, life cycle, and content quality. You will work with multiple engineering, software, economics and business teams to specify requirements, define data collection, interpretation strategies, data pipelines and implement data analysis and reporting tools. Your focus will be in optimizing the analysis of test results to enable the efficient growth of the Creator channel. You should be able to operate with a high level of autonomy and possess strong communication skills, both written and verbal. You have a combination of strong data analytical skills and business acumen, and a demonstrable ability to operate at scale. You can influence up, down, and across and thrive in entrepreneurial environments. You will excel at hiring and retaining a team of top performers, set strategic direction, build bridges with stakeholders, and cultivate a culture of invention and collaboration. Key job responsibilities · Own and prioritize the science and BI roadmaps for the Creator channel, working with our agile developers to size, scope, and weigh the trade-offs across that roadmap and other areas of the business. · Lead a team of data scientists and engineers skilled at using a variety of techniques including classic analytic techniques as well Data Science and Machine Learning to address hard problems. · Deeply understand the creator, their needs, the business landscape, and backend technologies. · Partner with business and engineering stakeholders across multiple teams to gather data/analytics requirements, and provide clear guidance to the team on prioritization and execution. · Run frequent experiments, in partnership with business stakeholders, to inform our innovation plans. · Ensure high availability for data infrastructure and high data quality, partnering with upstream teams as required.
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Research Scientist specializing the design of microwave components for cryogenic environments. Working alongside other scientists and engineers, you will design and validate hardware performing microwave signal conditioning at cryogenic temperatures for AWS quantum processors. Candidates must have a background in both microwave theory and implementation. Working effectively within a cross-functional team environment is critical. The ideal candidate will have a proven track record of hardware development from requirements development to validation. Key job responsibilities Our scientists and engineers collaborate across diverse teams and projects to offer state of the art, cost effective solutions for the signal conditioning of AWS quantum processor systems at cryogenic temperatures. You’ll bring a passion for innovation, collaboration, and mentoring to: Solve layered technical problems across our cryogenic signal chain. Develop requirements with key system stakeholders, including quantum device, test and measurement, cryogenic hardware, and theory teams. Design, implement, test, deploy, and maintain innovative solutions that meet both performance and cost metrics. Research enabling technologies necessary for AWS to produce commercially viable quantum computers. A day in the life As you design and implement cryogenic microwave signal conditioning solutions, from requirements definition to deployment, you will also: Participate in requirements, design, and test reviews and communicate with internal stakeholders. Work cross-functionally to help drive decisions using your unique technical background and skill set. Refine and define standards and processes for operational excellence. Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly. About the team 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.
US, WA, Bellevue
The Worldwide Design Engineering (WWDE) organization delivers innovative, effective and efficient engineering solutions that continually improve our customers’ experience. WWDE optimizes designs throughout the entire Amazon value chain providing overall fulfillment solutions from order receipt to last mile delivery. We are seeking a Simulation Scientist to assist in designing and optimizing the fulfillment network concepts and process improvement solutions using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be visionary technical expert and natural self-starter who have the drive to apply simulation and optimization tools to solve complex flow and buffer challenges during the development of next generation fulfillment solutions. The Simulation Scientist is expected to deep dive into complex problems and drive relentlessly towards innovative solutions working with cross functional teams. Be comfortable interfacing and influencing various functional teams and individuals at all levels of the organization in order to be successful. Lead strategic modelling and simulation projects related to drive process design decisions. Responsibilities: - Lead the design, implementation, and delivery of the simulation data science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages - Lead strategic modeling and simulation research projects to drive process design decisions - Be an exemplary practitioner in simulation science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards - Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact - Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations - Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects - Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments - Lead and actively participate in reviews of simulation research science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline - Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices - Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for data analysis , simulation, and developing modeling algorithms - Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout - Deliver results according to project schedules and quality Key job responsibilities • You influence the scientific strategy across multiple teams in your business area. You support go/no-go decisions, build consensus, and assist leaders in making trade-offs. You proactively clarify ambiguous problems, scientific deficiencies, and where your team’s solutions may bottleneck innovation for other teams. A day in the life The dat-to-day activities include challenging and problem solving scenario with fun filled environment working with talented and friendly team members. The internal stakeholders are IDEAS team members, WWDE design vertical and Global robotics team members. The team solve problems related to critical Capital decision making related to Material handling equipment and technology design solutions. About the team World Wide Design EngineeringSimulation Team’s mission is to apply advanced simulation tools and techniques to drive process flow design, optimization, and improvement for the Amazon Fulfillment Network. Team develops flow and buffer system simulation, physics simulation, package dynamics simulation and emulation models for various Amazon network facilities, such as Fulfillment Centers (FC), Inbound Cross-Dock (IXD) locations, Sort Centers, Airhubs, Delivery Stations, and Air hubs/Gateways. These intricate simulation models serve as invaluable tools, effectively identifying process flow bottlenecks and optimizing throughput. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data 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 like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques ML-India
US, WA, Seattle
Amazon's Global Fixed Marketing Campaign Measurement & Optimization (CMO) team is looking for a senior economic expert in causal inference and applied ML to advance the economic measurement, accuracy validation and optimization methodologies of Amazon's global multi-billion dollar fixed marketing spend. This is a thought leadership position to help set the long-term vision, drive methods innovation, and influence cross-org methods alignment. This role is also an expert in modeling and measuring marketing and customer value with proven capacity to innovate, scale measurement, and mentor talent. This candidate will also work closely with senior Fixed Marketing tech, product, finance and business leadership to devise science roadmaps for innovation and simplification, and adoption of insights to influence important resource allocation, fixed marketing spend and prioritization decisions. Excellent communication skills (verbal and written) are required to ensure success of this collaboration. The candidate must be passionate about advancing science for business and customer impact. Key job responsibilities - Advance measurement, accuracy validation, and optimization methodology within Fixed Marketing. - Motivate and drive data generation to size. - Develop novel, innovative and scalable marketing measurement techniques and methodologies. - Enable product and tech development to scale science solutions and approaches. A day in the life - Propose and refine economic and scientific measurement, accuracy validation, and optimization methodology to improve Fixed Marketing models, outputs and business results - Brief global fixed marketing and retails executives about FM measurement and optimization approaches, providing options to address strategic priorities. - Collaborate with and influence the broader scientific methodology community. About the team CMO's vision is to maximizing long-term free cash flow by providing reliable, accurate and useful global fixed marketing measurement and decision support. The team measures and helps optimize the incremental impact of Amazon (Stores, AWS, Devices) fixed marketing investment across TV, Digital, Social, Radio, and many other channels globally. This is a fully self supported team composed of scientists, economists, engineers, and product/program leaders with S-Team visibility. We are open to hiring candidates to work out of one of the following locations: Irvine, CA, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
GB, Cambridge
Our team builds generative AI solutions that will produce some of the future’s most influential voices in media and art. We develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video, with Amazon Game Studios and Alexa, the ground-breaking service that powers the audio for Echo. Do you want to be part of the team developing the future technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language, Audio and Video technology. As an Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and generative AI models to drive the state of the art in audio (and vocal arts) generation. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. * Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business. * Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
US, TX, Austin
The Workforce Solutions Analytics and Tech team is looking for a senior Applied Scientist who is interested in solving challenging optimization problems in the labor scheduling and operations efficiency space. We are actively looking to hire senior scientists to lead one or more of these problem spaces. Successful candidates will have a deep knowledge of Operations Research and Machine Learning methods, experience in applying these methods to large-scale business problems, the ability to map models into production-worthy code in Python or Java, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big research challenges. As a member of our team, you'll work on cutting-edge projects that directly impact over a million Amazon associates. This is a high-impact role with opportunities to designing and improving complex labor planning and cost optimization models. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. Key job responsibilities • Candidates will be responsible for developing solutions to better manage and optimize flexible labor capacity. The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning. As a senior scientist, you will also help coach/mentor junior scientists on the team. • In this role, you will be a technical leader in applied science research with significant scope, impact, and high visibility. You will lead science initiatives for strategic optimization and capacity planning. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. • Invent and design new solutions for scientifically-complex problem areas and identify opportunities for invention in existing or new business initiatives. • Successfully deliver large or critical solutions to complex problems in the support of medium-to-large business goals. • Apply mathematical optimization techniques and algorithms to design optimal or near optimal solution methodologies to be used for labor planning. • Research, prototype, simulate, and experiment with these models and participate in the production level deployment in Python or Java. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | Bellevue, WA, USA | Nashville, TN, USA | Seattle, WA, USA | Tempe, AZ, USA
US, WA, Bellevue
Amazon SCOT OIH (Supply Chain Optimization Technology - Optimal Inventory Health) team owns inventory health management for Retail worldwide. We use a dynamic programming model to maximize the net present value of inventory driving actions such as pricing markdowns, deals, removals, coupons etc. Our team, the OIH Insights Team energize and empower OIH business with the clarity and conviction required to make impactful business decisions through the generation of actionable and explainable insights, we do so through the following mechanisms: -- Transforming raw, complex datasets into intuitive, and actionable insights that impact OIH strategy and accelerate business decision making. -- Building and maintaining modular, scalable data models that provide the generality, flexibility, intuitiveness, and responsiveness required for seamless self-service insights. -- Generating deeper insights that drive competitive advantage using statistical modeling and machine learning. As a data scientist in the team, you can contribute to each layers of a data solution – you work closely with business intelligence engineers and product managers to obtain relevant datasets and prototype predictive analytic models, you team up with data engineers and software development engineers to implement data pipeline to productionize your models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. You will be diving deep in our data and have a strong bias for action to quickly produce high quality data analyses with clear findings and recommendations. The ideal candidate is self-motivated, has experience in applying technical knowledge to a business context, can turn ambiguous business questions into clearly defined problems, can effectively collaborate with research scientists, software development engineers, and product managers, and deliver results that meet high standards of data quality, security, and privacy. Key job responsibilities 1. Define and conduct experiments to optimize Long Term Free Cash Flow for Amazon Retail inventory, and communicate insights and recommendations to product, engineering, and business teams 2. Interview stakeholders to gather business requirements and translate them into concrete requirement for data science projects 3. Build models that forecast growth and incorporate inputs from product, engineering, finance and marketing partners 4. Apply data science techniques to automatically identify trends, patterns, and frictions of product life cycle, seasonality, etc 5. Work with data engineers and software development engineers to deploy models and experiments to production 6. Identify and recommend opportunities to automate systems, tools, and processes
US, WA, Seattle
At Amazon, a large portion of our business is driven by third-party Sellers who set their own prices. The Pricing science team is seeking a Sr. Applied Scientist to use statistical and machine learning techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems, helping Marketplace Sellers offer Customers great prices. This role will be a key member of an Advanced Analytics team supporting Pricing related business challenges based in Seattle, WA. The Sr. Applied Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Applied Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. An Applied Scientist at Amazon applies scientific principles to support significant invention, develops code and are deeply involved in bringing their algorithms to production. They also work on cross-disciplinary efforts with other scientists within Amazon. The key strategic objectives for this role include: - Understanding drivers, impacts, and key influences on Pricing dynamics. - Optimizing Seller Pricing to improve the Customer experience. - Drive actions at scale to provide low prices and increased selection for customers using scientifically-based methods and decision making. - Helping to support production systems that take inputs from multiple models and make decisions in real time. - Automating feedback loops for algorithms in production. - Utilizing Amazon systems and tools to effectively work with terabytes of data. You can also learn more about Amazon science here - https://www.amazon.science/