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A quick guide to Amazon's 65-plus papers at this year's ACL

Familiar topics such as question answering and natural-language understanding remain well represented, but a new concentration on language modeling and multimodal models reflect the spread of generative AI.

By Staff writer
July 10, 2023
10 min read
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Between the main conference and the recently inaugurated ACL Proceedings, Amazon researchers have more than 65 papers at this year's meeting of the Association for Computational Linguistics (ACL).

Automatic speech recognition

Masked audio text encoders are effective multi-modal rescorers*
Jason Cai, Monica Sunkara, Xilai Li, Anshu Bhatia, Xiao Pan, Sravan Bodapati

Code generation

A static evaluation of code completion by large language models
Hantian Ding, Varun Kumar, Yuchen Tian, Zijian Wang, Rob Kwiatkowski, Xiaopeng LI, Murali Krishna Ramanathan, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang

Multitask pretraining with structured knowledge for text-to-SQL generation
Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma

Code switching

Code-switched text synthesis in unseen language pairs*
I-Hung Hsu, Avik Ray, Shubham Garg, Nanyun Peng, Jing Huang

Code-switched text synthesis.png
From "Code-switched text synthesis in unseen language pairs".

CoMix: Guide transformers to code-mix using POS structure and phonetics*
Gaurav Arora, Srujana Merugu, Vivek Sembium

Continual learning

Characterizing and measuring linguistic dataset drift
Tyler A. Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas, Yassine Benajiba, Miguel Ballesteros, Dan Roth

Data-/table-to-text applications

An inner table retriever for robust table question answering
Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias

Few-shot data-to-text generation via unified representation and multi-source learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, JIE MA, Patrick Ng, Zhiguo Wang, Bonan Min, William Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

Improving cross-task generalization of unified table-to-text models with compositional task configurations*
Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Wang, Zhiheng Huang

LI-RAGE: Late interaction retrieval augmented generation with explicit signals for open-domain table question answering
Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, Gonzalo Iglesias

Dialogue

Diable: Efficient dialogue state tracking as operations on tables*
Pietro Lesci, Yoshinari Fujinuma, Momchil Hardalov, Chao Shang, Lluis Marquez

NatCS: Eliciting natural customer support dialogues
James Gung, Emily Moeng, Wesley Rose, Arshit Gupta, Yi Zhang, Saab Mansour

Schema-guided user satisfaction modeling for task-oriented dialogues
Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

Toward more accurate and generalizable evaluation metrics for task-oriented dialogs
Abi Komma, Nagesh Panyam, Timothy Leffel, Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas, Aram Galstyan

Explainable AI

Efficient Shapley values estimation by amortization for text classification
Alan Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang

Efficient Shapley values.png
From "Efficient Shapley values estimation by amortization for text classification".

Few shot rationale generation using self-training with dual teachers*
Aditya Srikanth Veerubhotla, Lahari Poddar, Jun Yin, Gyuri Szarvas, Sharanya Eswaran

Information extraction

An AMR-based link prediction approach for document-level event argument extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang, Qipeng Guo, Zheng Zhang

AVEN-GR: Attribute value extraction and normalization using product graphs
Donato Crisostomi, Thomas Ricatte

Large scale generative multimodal attribute extraction for e-commerce attributes
Anant Khandelwal, Happy Mittal, Shreyas Sunil Kulkarni, Deepak Gupta

ParaAMR: A large-scale syntactically diverse paraphrase dataset by AMR back-translation
Kuan-Hao Huang, Varun Iyer, I-Hung Hsu, Anoop Kumar, Kai-Wei Chang, Aram Galstyan

ParaAMR.png
From "ParaAMR: A large-scale syntactically diverse paraphrase dataset by AMR back-translation".

Weakly supervised hierarchical multi-task classification of customer questions
Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Mukku, Manan Soni, Rashmi Patange

WebIE: Faithful and robust information extraction on the web
Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni

Information retrieval

CUPID: Curriculum learning based real-time prediction using distillation
Arindam Bhattacharya, Ankith M S, Ankit Gandhi, Vijay Huddar, Atul Saroop, Rahul Bhagat

Direct fact retrieval from knowledge graphs without entity linking
Jinheon Baek, Alham Fikri Aji, Jens Lehmann, Sung Ju Hwang

Language modeling

Adaptation approaches for nearest neighbor language models*
Rishabh Bhardwaj, George Polovets, Monica Sunkara

CONTRACLM: Contrastive learning for causal language model
Nihal Jain, Dejiao Zhang, Wasi Ahmad, Zijian Wang, Feng Nan, Xiaopeng LI, Ming Tan, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Bing Xiang

Controlled text generation with hidden representation transformations*
Vaibhav Kumar, Hana Koorehdavoudi, Masud Moshtaghi, Amita Misra, Ankit Chadha, Emilio Ferrara

KILM: Knowledge injection into encoder-decoder language models
Yan XU, Mahdi Namazifar, Devamanyu Hazarika, Aishwarya Padmakumar, Yang Liu, Dilek Hakkani-Tür

KILM.png
From "KILM: Knowledge injection into encoder-decoder language models".

ReAugKD: Retrieval-augmented knowledge distillation for pre-trained language models
Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen

Recipes for sequential pre-training of multilingual encoder and seq2seq models*
Saleh Soltan, Andy Rosenbaum, Tobias Falke, Qin Lu, Anna Rumshisky, Wael Hamza

Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal, Sravan Bodapati, Katrin Kirchhoff, Dan Roth

Machine learning

Mitigating the burden of redundant datasets via batch-wise unique samples and frequency-aware losses
Donato Crisostomi, Andrea Caciolai, Alessandro Pedrani, Alessandro Manzotti, Enrico Palumbo, Kay Rottmann, Davide Bernardi

Machine translation

RAMP: Retrieval and attribute-marking enhanced prompting for attribute-controlled translation
Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu, Anna Currey, Georgiana Dinu, Maria Nădejde

Multimodal models

Benchmarking diverse-modal entity linking with generative models*
Sijia Wang, Alexander Li, Henry Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, JIE MA, William Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Generate then select: Open-ended visual question answering guided by world knowledge*
Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henry Zhu, Yuhao Zhang, Alexander Hanbo Li, William Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

KG-FLIP: Knowledge-guided fashion-domain language-image pre-training for e-commerce
Qinjin Jia, Yang Liu, Shaoyuan Xu, Huidong Liu, Daoping Wu, Jinmiao Fu, Roland Vollgraf, Bryan Wang

Resolving ambiguities in text-to-image generative models
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta

Translation-enhanced multilingual text-to-image generation
Yaoyiran Li, Ching-Yun (Frannie) Chang, Stephen Rawls, Ivan Vulić, Anna Korhonen

Unsupervised melody-to-lyric generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Chenyang Tao, Gunnar Sigurdsson, Wenbo Zhao, Tagyoung Chung, Jing Huang, Violet Peng

Unsupervised melody-to-lyric.png
From "Unsupervised melody-to-lyric generation".

Natural-language processing

Multi-VALUE: A framework for cross-dialectal English NLP
Caleb Ziems, William Held, Jingfeng Yang, Jwala Dhamala, Rahul Gupta, Diyi Yang

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport*
Weijie Xu, Xiaoyu Jiang, Srinivasan Sengamedu, "SHS", Francis Iannacci, Jinjin Zhao

Natural-language understanding

ECG-QALM: Entity-controlled synthetic text generation using contextual Q&A for NER*
Karan Aggarwal, Henry Jin, Aitzaz Ahmad

Entity contrastive learning in a large-scale virtual assistant system
Jonathan Rubin, Jason Crowley, George Leung, Morteza Ziyadi, Maria Minakova

EPIC: Multi-perspective annotation of a corpus of irony
Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi

Measuring and mitigating local instability in deep neural networks*
Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan

Reducing cohort bias in natural language understanding systems with targeted self-training scheme
Thu Le, Gabriela Cortes Hernandez, Bei Chen, Melanie Bradford

Privacy

Controlling the extraction of memorized data from large language models via prompt-tuning
Mustafa Ozdayi, Charith Peris, Jack G. M. FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, Rahul Gupta

Query rewriting

Context-aware query rewriting for improving users’ search experience on e-commerce websites
Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao

Unified contextual query rewriting
Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy, Sawyer Shen, Edward Guo, Gokhan Tur

Question answering

Accurate training of web-based question answering systems with feedback from ranked users
Liang Wang, Ivano Lauriola, Alessandro Moschitti

Context-aware transformer pre-training for answer sentence selection
Luca Di Liello, Siddhant Garg, Alessandro Moschitti

Cross-Lingual Knowledge Distillation for answer sentence selection in low-resource languages*
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti

Exploiting abstract meaning representation for open-domain question answering*
Cunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu, Xuefeng Bai, Zheng Zhang, Yue Zhang

Exploiting abstract meaning representation.png
From "Exploiting abstract meaning representation for open-domain question answering".

Hybrid hierarchical retrieval for open-domain question answering*
Manoj Ghuhan Arivazhagan, Lan Liu, Peng Qi, Xinchi Chen, William Wang, Zhiheng Huang

Learning answer generation using supervision from automatic question answering evaluators
Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti

RobustQA: Benchmarking the robustness of domain adaptation for open-domain question answering*
Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Wang, Zhiheng Huang, Bing Xiang, Dan Roth

Reasoning

FolkScope: Intention knowledge graph construction for e-commerce commonsense discovery*
Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin

SCOTT: Self-consistent chain-of-thought distillation
Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, Xiang Ren

Self-learning

Constrained policy optimization for controlled self-learning in conversational AI systems
Mohammad Kachuee, Sungjin Lee

Scalable and safe remediation of defective actions in self-learning conversational systems
Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu, Jae Do

Semantic parsing

An empirical analysis of leveraging knowledge for low-resource task-oriented semantic parsing*
Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon Des Mesnards, Sahar Movaghati, Mukund Harakere, He Xie, Jianhua Lu

XSEMPLR: Cross-lingual semantic parsing in multiple natural languages and meaning representations
Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang

XSemPLR.png
From "XSEMPLR: Cross-lingual semantic parsing in multiple natural languages and meaning representations".

Spoken-language understanding

Regression-free model updates for spoken language understanding
Andrea Caciolai, Verena Weber, Tobias Falke, Alessandro Pedrani, Davide Bernardi

Sharing encoder representations across languages, domains and tasks in large-scale spoken language understanding
Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Satya Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev, Mariusz Momotko, Denis Romasanta Rodriguez, Austin Doolittle, Kartik Balasubramaniam, Wael Hamza, Fabian Triefenbach, Patrick Lehnen

Toxic-language classification

QCon at SemEval-2023 Task 10: Data augmentation and model ensembling for detection of online sexism
Wes Feely, Prabhakar Gupta, Manas Mohanty, Tim Chon, Tuhin Kundu, Vijit Singh, Sandeep Atluri, Tanya Roosta, Viviane Ghaderi, Peter Schulam, Heba Elfardy

Towards building a robust toxicity predictor
Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Yanjun (Jane) Qi

*Accepted to ACL Findings

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US, WA, Seattle
AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models large language models and agentic reasoning systems to life sciences problems then put them into the hands of customers as applications and managed services they can fine-tune tailor and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological regulatory and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor encourage publication and support conference participation. If you want to do research that ships this is the team. The Role We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products with a primary focus on clinical trial operations and agentic reasoning. You will design train and evaluate systems that reason over complex clinical and operational logic and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems own your results end to end and see your work reach production. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason plan and act in complex scientific domains while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: • How do you build LLM-based agentic systems that correctly reason over clinical protocols regulatory standards and complex multi-step operational workflows? • How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? • How do you develop model customization methods (fine-tuning retrieval augmentation domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation structured reasoning evaluation domain adaptation) with opportunities to contribute across drug discovery (protein engineering antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production and your work will directly shape the tools that scientists use daily. Key job responsibilities • Design train fine-tune and evaluate LLM-based agentic systems that reason over clinical protocols regulatory standards and operational workflows • Build rigorous evaluation harnesses and benchmarks to measure agent reliability faithfulness and failure modes in high-stakes domains • Develop model customization methods (fine-tuning RLHF retrieval augmentation domain adaptation) that help customers get better outputs on their own data with less effort • Contribute to graph-based and causal modeling approaches for clinical trial operations • Partner with Life Sciences domain experts product and engineering to translate scientific challenges into shipped capabilities • Own experiments end to end: problem framing implementation evaluation iteration and hand-off to production • Publish at top-tier venues where the work supports it • Contribute to drug discovery efforts (protein engineering antibody design) as opportunities arise A day in the life • Design and run an experiment to validate a new agentic reasoning or fine-tuning method then ship it as a capability customers can use • Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone • Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic • Meet with domain experts to scope what the next model release needs to do • Review results with a senior scientist sharpen the approach and get it over the finish line • Prototype a new idea that could become the next capability in the product About the team Amazon values diverse experiences. Even if you do not meet all of the preferred 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. Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness. We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Data Scientist III, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. Key job 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 ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications • 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 • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems. About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
Data Scientist II, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. Key job 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 • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
Data Scientist III, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. Key job 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 ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications • 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 • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems. About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
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