The breadth of Amazon's computer vision research is on display at ECCV

Research topics range from visual anomaly detection to road network extraction, regression-constrained neural-architecture search to self-supervised learning for video representations.

Amazon's contributions to this year's European Conference on Computer Vision (ECCV) reflect the diversity of the company's research interests. Below is a quick guide to the topics and methods of a dozen ECCV papers whose authors include Amazon scientists.

Fine-grained fashion representation learning by online deep clustering
Yang (Andrew) Jiao, Ning Xie, Yan Gao, Chien-Chih Wang, Yi Sun

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Fashions are characterized by both global attributes, such as “skirt length”, and local attributes, such as “neckline style”. Accurate representations of such attributes are essential to tasks like fashion retrieval and fashion recommendation, but learning representations of each attribute independently ignores shared visual statistics among the attributes. Instead, the researchers treat representation learning as a multitask learning problem, enforcing cluster-level constraints on global structure. The learned representations improve fashion retrieval by a large margin.

GLASS: Global to local attention for scene-text spotting
Roi Ronen, Shahar Tsiper, Oron Anschel, Inbal Lavi, Amir Markovitz, R. Manmatha

Modern text-spotting models combine text detection and recognition into a single end-to-end framework, in which both tasks often rely on a shared global feature map. Such models, however, struggle to recognize text across scale variations (smaller or larger text) and arbitrary word rotation angles. The researchers propose a novel attention mechanism for text spotting, called GLASS, that fuses together global and local features. The global features are extracted from the shared backbone, while the local features are computed individually on resized, high-resolution word crops with upright orientation. GLASS achieves state-of-the-art results on multiple public benchmarks, and the researchers show that it can be integrated with other text-spotting solutions, improving their performance.

GLASS.png
A novel attention mechanism for text spotting, called GLASS, fuses together global and local features. From "GLASS: Global to local attention for scene-text spotting".

Large scale real-world multi-person tracking
Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi, Alyssa Boden, Joseph Tighe

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This paper presents a new multi-person tracking dataset — PersonPath22 — which is more than an order of magnitude larger than existing high-quality multi-object tracking datasets. The PersonPath22 dataset is specifically sourced to provide a wide variety of conditions, and its annotations include rich metadata that allows the performance of a tracker to be evaluated along these different dimensions. Its large-scale real-world training and test data enable the community to better understand the performance of multi-person tracking systems in a range of scenarios and conditions.

MaCLR: Motion-aware contrastive Learning of representations for videos
Fanyi Xiao, Joseph Tighe, Davide Modolo

Attempts to use self-supervised learning for video have had some success, but existing approaches don’t make explicit use of motion information derived from the temporal sequence, which is important for supervised action recognition tasks. The researchers propose a self-supervised video representation-learning method that explicitly models motion cues during training. The method, MaCLR, consists of two pathways, visual and motion, connected by a novel cross-modal contrastive objective that enables the motion pathway to guide the visual pathway toward relevant motion cues.

MACLR.png
A frame of video (top left) and three different methods of capturing motion. From "MaCLR: Motion-aware contrastive Learning of representations for videos".

PSS: Progressive sample selection for open-world visual representation learning
Tianyue Cao, Yongxin Wang, Yifan Xing, Tianjun Xiao, Tong He, Zheng Zhang, Hao Zhou, Joseph Tighe

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New end-to-end approach to zero-shot video classification dramatically outperforms predecessors.

In computer vision, open-world representation learning is the challenge of learning representations for categories of images not seen during training. Existing approaches make unrealistic assumptions, such as foreknowledge of the number of categories the unseen images fall into, or the ability to determine in advance which unlabeled training examples fall into unseen categories. The researchers’ novel progressive approach avoids such assumptions, selecting at each iteration unlabeled samples that are highly homogenous but belong to classes that are distant from the current set of known classes. High-quality pseudo-labels generated via clustering over these selected samples then improve the feature generalization iteratively.

Rayleigh EigenDirections (REDs): Nonlinear GAN latent space traversals for multidimensional features
Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez, Pietro Perona

Generative adversarial networks (GANs) can map points in a latent space to images, producing extremely realistic synthetic data. Past attempts to control GANs’ outputs have looked for linear trajectories through the space that correspond, approximately, to continuous variation of a particular image feature. The researchers propose a new method for finding nonlinear trajectories through the space, providing unprecedented control over GANs’ outputs, including the ability to hold specified image features fixed while varying others.

Rethinking few-shot object detection on a multi-domain benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

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New “meta-learning” approach improves on the state of the art in “one-shot” learning.

Most existing work on few-shot object detection (FSOD) focuses on settings where both the pretraining and few-shot learning datasets are from similar domains. The researchers propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms across a greater variety of applications. They comprehensively analyze the effects of freezing layers, different architectures, and different pretraining datasets on FSOD performance, drawing several surprising conclusions. One of these is that, contrary to prior belief, on a multidomain benchmark, fine-tuning (FT) is a strong baseline for FSOD.

SPot-the-Difference: Self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer

Visual anomaly detection is commonly used in industrial quality inspection. This paper presents a new dataset and a new self-supervised learning method for ImageNet pretraining to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. The Visual Anomaly (VisA) Dataset consists of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in three domains, making it one of the largest industrial anomaly detection datasets to date. The paper also proposes a new self-supervised framework — SPot-the-Difference (SPD) — that can regularize contrastive self-supervised and also supervised pretraining to better handle anomaly detection tasks.

SPD contrastive learning.png
Conventional contrastive learning (left) and the contrastive-learning scheme used in SPD (spot-the-difference) training. From "SPot-the-difference: Self-supervised pre-training for anomaly detection and segmentation".

TD-Road: Top-down road network extraction with holistic graph construction
Yang He, Ravi Garg, Amber Roy Chowdhury

Road network extraction from satellite imagery is essential for constructing rich maps and enabling numerous applications in route planning and navigation. Previous graph-based methods used a bottom-up approach, estimating local information and extending a graph iteratively. This paper, by contrast, proposes a top-down approach that decomposes the problem into two subtasks: key point prediction and connectedness prediction. Unlike previous approaches, the proposed method applies graph structures (i.e., locations of nodes and connections between them) as training supervisions for deep neural networks and directly generates road graph outputs through inference.

TD-road.png
A satellite image (left) and three methods for extracting road networks from it: segmentation, bottom-up-graph-based methods, and a new top-down graph-based method (far right). From "TD-Road: Top-down road network extraction with holistic graph construction."

Towards regression-free neural networks for diverse compute platforms
Rahul Duggal, Hao Zhou, Shuo Yang, Jun Fang, Yuanjun Xiong, Wei Xia

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New approach corrects for cases when average improvements are accompanied by specific regressions.

Commercial machine learning models are constantly being updated, and while an updated model may improve performance on average, it can still regress — i.e., suffer “negative flips” — on particular inputs it used to handle correctly. This paper introduces regression-constrained neural-architecture search (REG-NAS), which consists of two components: (1) a novel architecture constraint that enables a larger model to contain all the weights of a smaller one, thus maximizing weight sharing, and (2) a novel search reward that incorporates both top-1 accuracy and negative flips in the architecture search metric. Relative to the existing state-of-the-art approach, REG-NAS enables 33 – 48% reduction of negative flips.

Unsupervised and semi-supervised bias benchmarking in face recognition
Alexandra Chouldechova, Siqi Deng, Yongxin Wang, Wei Xia, Pietro Perona

This paper introduces semi-supervised performance evaluation for face recognition (SPE-FR), a statistical method for evaluating the performance and algorithmic bias of face verification systems when identity labels are unavailable or incomplete. The method is based on parametric Bayesian modeling of face embedding similarity scores, and it produces point estimates, performance curves, and confidence bands that reflect uncertainty in the estimation procedure. Experiments show that SPE-FR can accurately assess performance on data with no identity labels and confidently reveal demographic biases in system performance.

X-DETR: A versatile architecture for instance-wise vision-language tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, Stefano Soatto

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This paper addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. The paper presents the X-DETR model, whose architecture has three major components: an object detector, a language encoder, and a vision-language alignment module. The vision and language streams are independent until the end, and they are aligned using an efficient dot-product operation. This simple architecture shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, such as open-vocabulary object detection.

X-DETR.png
X-DETR addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. From "X-DETR: A versatile architecture for instance-wise vision-language tasks".

Research areas

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US, WA, Seattle
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US, WA, Seattle
Are you excited about developing generative AI and foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a Applied Scientist to focus on large vision and manipulation machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes using machine learning to drive hardware movement. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. About the team This team invents and runs robots focused on grasping and packing items. These are typically 6-dof style robotic arms. Our work ranges from the long-term-research on basic science to deploying/supporting large production fleets handling billions of items per year. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon launched the Generative AI (GenAI) Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate enterprise innovation and success with Generative AI (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). Customers such as Highspot, Lonely Planet, Ryanair, and Twilio are engaging with the GAI Innovation Center to explore developing generative solutions. GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As a data scientist at GAIIC, you are proficient in designing and developing advanced Generative AI based solutions to solve diverse customer problems. You will be working with terabytes of text, images, and other types of data to solve real-world problems through Gen AI. You will be working closely with account teams and ML strategists to define the use case, and with other scientists and ML engineers on the team to design experiments, and find new ways to deliver value to the customer. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners. This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. About the team 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. 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
Amazon’s mission is to be the most customer centric company in the world. The Workforce Staffing (WFS) organization is on the front line of that mission by hiring the hourly fulfillment associates who make that mission a reality. To drive the necessary growth and continued scale of Amazon’s associate needs within a constrained employment environment, Amazon has created the Workforce Intelligence (WFI) team. This team will (re)invent how Amazon attracts, communicates with, and ultimately hires its hourly associates. This team owns multi-layered research and program implementation to drive deep learning, process improvements, and strategic recommendations to global leadership. Are you passionate about data? Do you enjoy questioning the status quo? Do complex and difficult challenges excite you? If yes, this may be the team for you. The Data Scientist will be responsible for creating cutting edge algorithms, predictive and prescriptive models as well as required data models to facilitate WFS at-scale warehouse associate hiring. This role acts as an internal consultant to the marketing, biz ops and candidate experience teams covering responsibilities such as at-scale hiring process improvement, analyzing large scale candidate/associate data and being strategic to providing best candidate hiring experience to WFS warehouse associate candidates. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, CA, Sunnyvale
At Amazon Fashion, we are obsessed with making Amazon Fashion the most loved fashion destinations globally. We're searching for Computer Vision pioneers who are passionate about technology, innovation, and customer experience, and who are enthusiastic about making a lasting impact on the industry. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey and change the world of eCommerce forever Key job responsibilities As a Applied Scientist, you will be at the forefront to define, own and drive the science that span multiple machine learning models and enabling multiple product/engineering teams and organizations. You will partner with product management and technical leadership to identify opportunities to innovate customer facing experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but more importantly influence strategy and outcomes across different Amazon organizations such as Search, Personalization and more. This role is inherently cross-functional and requires a strong ability to communicate, influence and earn the trust of software engineers, technical and business leadership. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problem in the Amazon scale? Are you excited by developing and productizing machine learning, deep learning algorithms and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring Applied Scientist who has a solid background in applied Machine Learning and a proven record of solving customer-facing problems via scalable ML solutions, and is motivated to grow professionally as an ML scientist. Key job responsibilities - Tackle ambiguous problems in Machine Learning and drive full life-cycle Machine Learning projects. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. - Run A/B experiments, gather data, and perform statistical tests. - Establish scalable, efficient, automated processes for large-scale data mining, machine-learning model development, model validation and serving. - Work closely with software engineers and product managers to assist in productizing your ML models. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, WA, Bellevue
Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! The Network Planning and Fulfillment Execution team owns and operates OR/ML and simulation systems that continually optimize the distribution of tens of millions of products across Amazon’s warehouses in the most cost-effective manner, utilizing large scale optimization techniques and distributed computing in trying to reduce overall transportation costs while improving the customer experience. We are focused on saving hundreds of millions of dollars using big data technologies, cutting edge science, machine learning, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply. We’re looking for a passionate, results-oriented, and inventive Research Scientist who can create and improve OR/ML models for our outbound transportation planning systems. In addition, you will be working on design, development and evaluation of highly innovative OR and ML models for solving complex business problems in the area of outbound transportation planning systems. More specifically, you will be developing a Mathematical Optimization model towards short term Origin-Destination flows that are inventory aware and adhere to facility capacities given destination demand. This will also require you to build machine learning models to predict inventory N weeks out (N<13 Weeks) and ML models to calibrate inventory bounds and math model errors. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create ML solutions to solve those problems at scale. You will directly impact our direct customers, and even play with big data and incredible scale in the background. Watch http://bit.ly/amazon-scot to get the big picture. Key job responsibilities As part of your daily work you will: * Design, development and evaluation of highly innovative OR/ML models for solving complex business problems. * Analyze and extract relevant information from large amounts of data to help automate and optimize key processes. * Research and apply the latest ML techniques and best practices from both academia and industry. * Think about customers and how to improve the customer delivery experience. * Use and analytical techniques to create scalable solutions for business problems. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. * Technically lead and mentor other scientists in team. * Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within it's logistics network including Fulfillment Centers, Sort Centers, Delivery Stations, and more. In this role, you will be a design and develop Optimization and Machine Learning models with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. 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. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Network Planning and Fulfillment Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Sunnyvale
Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The mission of Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized fit and size recommendation when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights. - You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders. - You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders. - You thrive in an agile and fast-paced environment on highly visible projects and initiatives. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
US, WA, Seattle
Amazon is continuing to invest in its Advertising business to tap into the growing online advertising market. The Publisher Technologies team builds and operates extensible services that empower 1P Publishers to improve the monetization of their customer experiences, along with the experiences themselves. We bias toward standards-based and flexible designs that allow Publishers the ability to invent on top of our solutions and to interoperate well with other advertising technology providers; both internal and external. The Publisher Technology Data, Insights, and Analytics team enables faster data-driven decision making for Publishers and Monetization teams by providing them with near real time data, data management tools, actionable insights, and an easy-to-use reporting experience. Our data products provide Publishers and Monetization teams with the capabilities necessary to better understand the performance of their Advertising products along with supporting machine learning at scale. In this role, you will join a team whose data products and services empower hundreds of teams across Amazon with near real time data to support big data analytics, insights, and machine learning at scale. You will collaborate with cross-functional teams to design, develop, and implement advanced data tools, predictive models, and machine learning algorithms to support Advertising strategies and optimize revenue streams. You will analyze large-scale data to identify patterns and trends, and design and run A/B experiments to improve Publisher and advertiser experiences. Key job responsibilities - Design and lead large projects and experiments from beginning to end, and drive solutions to complex or ambiguous problems - Create tools and solve challenges using statistical modeling, machine learning, optimization, and/or other approaches for quantifiable impact on the business - Use broad expertise to recommend the right strategies, methodologies, and best practices, teaching and mentoring others - Key influencer of your team’s business strategy and of related teams’ strategies - Communication and documentation of methodologies, insights, and recommendations for senior leaders with various levels of technical knowledge We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA