elena2.png
Elena Ehrlich, a principal data scientist at Amazon Web Services, works on, among other things, time-series modeling. It was her work in that area that caught the attention of the National Football League, which led to a new passing metric.

Using data science to help improve NFL quarterback passing scores

Principal data scientist Elena Ehrlich uses her skills to help a wide variety of customers — including the National Football League.

In any given month as a principal data scientist at Amazon Web Services (AWS), Elena Ehrlich might be working on time-series modeling, a computer vision project, a natural language processing problem, and more. Her work within the AWS Professional Services organization involves solving data problems for customers in fields ranging from media to energy to sports.

Spliced binned-Pareto distributions, developed in part by Elena Ehrlich, are flexible enough to handle symmetric, asymmetric, and multimodal distributions, offering a more consistent metric.

Sometimes a customer has a particular model in mind and will consult with Ehrlich's team to build or refine it. Often, though, they are not so far along. They simply have a business problem to solve. Ehrlich works with them anywhere from two months to three years to develop a solution that the client can then maintain going forward.

Ehrlich likes the ability to apply data science across a variety of verticals without having to switch jobs, or even teams.

"Amazon has a large and diverse landscape of customers, so I can gain a lot of different domain knowledge," Ehrlich says. "Each customer's needs are unique, which makes it very interesting, and the challenge is to come up with solutions that can be reused by other customers as well.”

Better predictions for spiky time series

Ehrlich's work with the NFL is one example of science applied to business challenges. Independent of her team's existing work with the league, she and colleagues developed a method for modeling heavy-tailed time series. In these sequences, one can have dramatic, unpredictable spikes: Think extreme rainfall events that shape totals over the course of a year, or a product suddenly going viral, increasing demand.

An NFL Next Gen Stats video screengrab shows Rams quarterback Matthew Stafford preparing to make a pass
The NFL's new Passing Score was developed using Ehrlich's Spliced Binned-Pareto method. It can place a quarterback's performance, such as that of Matthew Stafford, within the context of expected performance across the league.

Many statistical methods that would perform fine on more uniform curves falter when it comes to the noise of heavy-tailed time series. Yet being able to characterize these tails is important. On an EKG, for instance, you must be able to tell whether a peak in heart rate signals disease or simply the beginning of a workout.

Predictive models were not able to reliably pinpoint such anomalies. Over the course of a few months, Ehrlich and Amazon researchers Francois-Xavier Aubet and Laurent Callot developed a solution, which they presented at the 2021 International Conference on Learning Representations' RobustML Workshop.

"If you're seeing an issue from a few customers, then it's worth zooming out, solving it as a research problem, and then going back to examine to which customers this can apply," Ehrlich said.

Related content
In its collaboration with the NFL, AWS contributes cloud computing technology, machine learning services, business intelligence services — and, sometimes, the expertise of its scientists.

Their solution, the Spliced Binned-Pareto method, combines two statistical techniques, the binned and the Pareto distributions. The latter stems from Italian economist Vilfredo Pareto's 80/20 rule: The 1920s idea that 80% of outcomes emerge from 20% of the causes (most of a country's wealth attributable to a fifth of its population, for example). This power-law relationship, when generalized, delivered the Second Theorem of Extreme Value Theory in 1975, which states that any and all distribution tails can be well approximated by a Generalized Pareto Distribution.

The researchers combined this with binned distribution, which discretizes regions within a larger dataset. Their method effectively cordons off and zeroes in on the spikes within a time series, leading to an improved ability to accommodate these extremes and calibrate estimates of them over time, which in turn leads to more accurate heavy-tail predictions.

This work aligned with a request from the NFL. While quarterback ratings exist in various forms, the league wanted a metric to rate passing performance. But a meaningful passing metric had to extend beyond passing yards, touchdowns, and interceptions to reflect the degree of difficulty for those outcomes given the specific play’s circumstances, in order to evaluate the NFL quarterback’s performance.

In January, the National Football League announced its new QB passing score, which addressed the inconsistency across plays, games, weeks, and seasons found in previous scores. A method based on spliced binned-Pareto distributions, developed by Amazon researchers, led to the improved passing metric.

The resulting NFL Passing Score, developed using Ehrlich's Spliced Binned-Pareto method, can place a quarterback's performance within the context of expected performance across the league.

That's because it is capable of estimating that heavy tail — in this case, those exceptional moments in a quarterback's throw — and assign them the proper weight toward the overall score. The NFL debuted the Passing Score early this year, ahead of the Super Bowl. Perhaps not surprisingly, Green Bay Packers quarterback Aaron Rodgers had the highest score.

An early affinity for math

Though the Passing Score project is complete, Ehrlich continues to refine the Spliced Binned-Pareto distribution, among other facets of her work.

"You want to forge a team to be at the leading edge of industry," she says. "Leading edge is determined by how short the lag is between your academic progress and marketplace usage."

Ehrlich has bachelor's and master's degrees in mathematics and a doctorate degree in statistics, all from Imperial College London — a school she chose in part because its program allowed her to focus solely on mathematics from her first year of university.

Related content
Danielle Maddix Robinson's mathematics background helps inform robust models that can predict everything from retail demand to epidemiology.

The predilection toward math and science runs in the family: Ehrlich's father is a mathematician, her mother a physicist. Numbers were a part of childhood, growing up in New Jersey. On skiing trips, she recalls, her father would point out the numbers on chair lifts and challenge her to factor them or shout "prime." If she got them all right, she'd get a candy reward.

"I thought these were fun games," Ehrlich says.

Ehrlich is glad to have had a singular focus on mathematics from undergrad onward.

"My career is where I went to get breadth and width, but it was really helpful to have this depth," she says. "It helps me learn faster when I get to a new domain or application, just having the technical strength."

'Genuinely excited about the problems'

Ehrlich embarked on her PhD, which focused on state-space models with applications for aerospace and missile tracking, thinking she would stay in academia. But as she completed the work in 2014, she knew there was an emerging job market for skills like hers.

"The real world had an appreciation for methodologies that weren't exactly instant," she says. "It takes some time to research good solutions and test out their longevity and experiment their generalizability."

She held research scientist positions at companies including IBM and Winton Capital Management before joining Amazon in 2017. Heading to Seattle headquarters, she prepared for some housekeeping-type meetings. She was surprised to find opportunities to sign up for dev ops and other classes related to Amazon technology. It felt like being back at university, she says, in a good way. The culture reflects this drive to learn; in fact, one of the company’s leadership principles is “learn and be curious.”

Related content
Amazon's Daliana Liu helps others in the field chart their own paths.

"People that come to work here are genuinely excited about the problems," she says. "It makes for more data-driven conversation based on the problem at hand. That intersects with the fact that since Amazon is big with a wide-range of opportunities, it attracts a lot of top talent."

An early project for Ehrlich focused on 21st Century Fox. She developed and implemented an optimized Ad Sales Pricing platform for the company's advertising time spots, horizontally scaling to match potential advertisers with spots across millions of opportunities. Working with sales and engineering teams, she moved the algorithm into production, boosting revenue for Fox.

For students who are interested in a career like Ehrlich's, she recommends starting with first principles and then confirming that understanding with actual projects.

"You should have some corner of theory really well understood. Then iterate between knowing something and implementing it — it doesn't even matter how small," she says. "This is a very fast, but very solid, way to grow."

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.