A screenshot from SageMaker Clarify
SageMaker Clarify is integrated with Amazon SageMaker Data Wrangler, making it easier to identify bias during data preparation. You specify attributes of interest, such as gender or age, and SageMaker Clarify runs a set of algorithms to detect any presence of bias in those attributes.
Credit: AWS

How Clarify helps machine learning developers detect unintended bias

Learn why the science team behind Clarify turned to a concept from 1951 to address a modern complexity.

In his machine learning keynote at re:Invent on Tuesday, Swami Sivasubramanian, vice president of machine learning, Amazon Web Services (AWS), announced Amazon SageMaker Clarify, a new service that helps customers detect statistical bias in their data and machine learning models, and helps explain why their models are making specific predictions. Clarify saves developers time and effort by providing them the ability to better understand and explain how their machine learning models arrive at their predictions.

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining

Developers today contend with both increasingly large volumes of data, as well as more complex machine learning models. In order to detect bias in those complex models and data sets, developers must rely on open-source libraries replete with custom code recipes that are inconsistent across machine learning frameworks. This tedious approach requires a lot of manual effort and often arrives too late to correct unintended bias.

“If you care about this stuff, it's pretty much a roll-your-own situation right now,” said University of Pennsylvania computer science professor and Amazon Scholar Michael Kearns, who provided guidance to the team of scientists that developed SageMaker Clarify. “If you want to do some practical bias detection, you either need to implement it yourself or go to one of the open-source libraries, which vary in quality. They're frequently not well-maintained or documented. In many cases, it's just, ‘Here is the code we used to run our experiments for this academic paper, good luck.’”

SageMaker Clarify helps address the challenges of relying on multiple open-source libraries by offering robust, reliable code in an integrated, cloud-based framework.

Increasingly complex networks

The efficacy of machine learning models depends in part on understanding how much influence a given input has on the output.

AWS on Air 2020: AWS What’s Next ft. Amazon SageMaker Clarify

“A lending model for consumer loans might include credit history, employment history, and how long someone has lived at their current address,” Kearns explained. “It might also utilize variables that aren't specifically financial, such as demographic variables. One thing you might naturally want to know is which of these variables is more important in the model’s predictions, which may be used in lending decisions, and which are less important.”

With linear models, each variable is assigned some weight, positive or negative, and the overall decision is a sum of those weighted inputs. In those cases, the inputs with the bigger weights clearly have more influence on the output.

However, that approach falls short with neural networks or more complicated, non-linear models. “When you get to models like neural networks, it's no longer a simple matter of determining or measuring the influence of an input on the output,” Kearns said.

To help account for the growing complexity of modern machine learning models, the Amazon science team looked to the past — specifically to an idea from 1951.

Shapley values

The team wanted to design a solution to help machine learning pros be able to better explain their models’ decisions in the face of growing complexity. They found inspiration in a popular scientific method called Shapley values.

Shapley values were named in honor of Lloyd Shapley, who introduced the idea in 1951 and who won the Nobel Prize in Economics for it in 2012. The Shapley value approach, which is rooted in game theory, considers a wide range of possible inputs and outputs and offers “the average marginal contribution of a feature value across all possible coalitions”.  The comprehensive nature of the approach means it can help provide a framework for understanding the relative weight of a set of inputs, even across complex models and multiple inputs.

“SageMaker Clarify utilizes Shapley values to essentially take your model and run a number of experiments on it or on your data set,” Kearns said. “It then uses that to help come up with a visualization and quantification of which of those inputs is more or less important.”

Nor does it matter which kind of model a developer uses. “One of the nice things about this approach is it is model agnostic,” Kearns said. “It performs input-output experiments and gives you some sense of the relative importance of the different inputs to the output decision.”

The science team also worked to be certain SageMaker Clarify had a comprehensive view. They designed it so everyday developers and data scientists can detect bias across the entire machine learning workflow — including data preparation, training, and inference. SageMaker Clarify is able to achieve that comprehensive view, Kearns explained, because (again) it is model agnostic. “Each of these steps has been designed to avoid making strong assumptions about the type of model that the user is building.”

Bias detection and explainability

Model builders who learn that their models are making predictions that are strongly correlated to a specific input may find those predictions fall short of their definition of fairness. Kearns offered the example of a lending company that discovers its model’s predictions are skewed. “That company will want to understand why its model is making predictions that might lead to decisions to give loans at a lower rate to group A than to group B, even if they're equally credit worthy.”

SageMaker Clarify can examine tabular data and help the modelers spot where gaps might exist. “This company would upload a spreadsheet of data showing who they gave loans to, what they knew about them, et cetera,” Kearns said. “What the data bias detection part does is say, ‘For these columns, there may be over or underrepresentation of certain features, which could lead to a discriminatory outcome if not addressed.’”

A screenshot from SageMaker Clarify
SageMaker Clarify is integrated with SageMaker Model Monitor, enabling you to configure alerting systems like Amazon CloudWatch to notify you if your model exceeds certain bias metric thresholds. 
Credit: AWS

That can be influenced by a number of factors, including simply lacking the correct data to build accurate predictions. For example, SageMaker Clarify can indicate whether modelers have enough data on certain groups of applicants to expect an accurate prediction. The metrics provided by SageMaker Clarify can then be used to correct unintended bias in machine learning models, and automatically monitor model predictions in production to help ensure they are not trending toward biased outcomes.

Future applications

The SageMaker Clarify science team is already looking to the future.

Their research areas include algorithmic fairness and machine learning, as well as explainable AI. Team members have published widely in the academic literature on these topics, and worked hard in the development of SageMaker Clarify to balance the science of fairness with engineering solutions and practical product design. Their approaches are both statistical and causal, and focus not only on bias measurement in trained models, but also bias mitigation. It is that last part that has Kearns particularly excited about the future.

“The ability to not just identify problems in your models, but also have the tools to train them in a different way would go a long way toward mitigating that bias,” he said. “It’s good to know that you have a problem, but it's even better to have a solution to your problem.”

Best practices

The notions of bias and fairness are highly application dependent and the choice of the attributes for which bias is to be measured, as well as the choice of the bias metrics, may need to be guided by social, legal, and other non-technical considerations,” said principal applied scientist Krishnaram Kenthapadi, who led the scientific effort behind SageMaker Clarify. “For successful adoption of fairness-aware machine learning and explainable AI approaches in practice, it’s important to build consensus and achieve collaboration across key stakeholders such as product, policy, legal, engineering, and AI/ML teams, as well as end users and communities,” he said. “Further, it’s good to take into account fairness and explainability considerations during each stage of the ML lifecycle, for example, Problem Formation, Dataset Construction, Algorithm Selection, Model Training Process, Testing Process, Deployment, and Monitoring/Feedback.

Find more best practices on the AWS website.

Research areas

Related content

US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Come work for M13 - an AWS team specializing in the deception and disruption of cyber threats. We are looking for an Applied Scientist who is passionate about the security domain. You will build services and tools for security engineers and developers that leverage artificial intelligence and machine learning to pull unique insights about the cyber threat landscape. You will be part of a team building Large Language Model (LLM)-based services with the focus on enabling AWS teams to interact with our threat data. The team works in close collaboration with other AWS security services to power mitigations that protect the global AWS network and features in external security services such as Amazon GuardDuty, AWS WAF, and AWS Network Firewall. If you are excited about combating the ever evolving threat landscape then we'd love to talk to you. As an Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. Key job responsibilities • Understand the challenges that security engineers and developers face when building software today, and develop generalizable solutions. • Collaborate with the team to pave the way towards bringing your solution into production systems. Lead cross team projects and ensure technical blockers are resolved • Communicate and document your research via publishing papers in external scientific venues. About the team *Why AWS* 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. *Diverse Experiences* 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. *Work/Life Balance* We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. *Inclusive Team Culture* 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 (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. *Mentorship and Career Growth* 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.
US, WA, Seattle
We are seeking a senior scientist with demonstrated experience in A/B testing along with related experience with observational causal modeling (e.g. synthetic controls, causal matrix completion). Our team owns "causal inference as a service" for the Pricing and Promotions organization; we run A/B tests on new pricing, promotions, and pricing/promotions CX algorithms and, where experimentation is impractical, conduct observational causal studies. Key job responsibilities We are seeking a senior scientist to help envision, design, and build the next generation of pricing, promotions, and pricing/promotions CX for Amazon. On our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design and implement in production new statistical methods for measuring causal effects of an extensive array of business policies. This position is perfect for someone who has a deep and broad analytic background, is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis and have experience coding with engineers to put projects into production. We are particularly interested in candidates with research background in experimental statistics. A day in the life - Discuss with business problems with business partners, product managers, and tech leaders - Brainstorm with other scientists to design the right model for the problem at hand - Present the results and new ideas for existing or forward looking problems to leadership - Dive deep into the data - Build working prototypes of models - Work with engineers to implement prototypes in production - Analyze the results and review with partners About the team We are a team of scientists who design and implement the econometrics powering pricing, promotions, and pricing/promotions CX.
US, WA, Seattle
Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLMs, etc. The ideal candidate has the ability to understand, implement, innovate and on the state-of-the-art generative AI based systems. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow, AWS Sagemaker, and SparkML. Our team is at an early stage, so you will have significant impact on our ML deliverables with little operational load from existing models/systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. Our team is leading ML and optimization features in Amazon Connect. We are a team of scientists and engineers working on multiple science projects for Amazon Connect. We use state-of-the-art science and engineering practices to address the hard problems in contact center operation and management for our customers, and we move fast to implement solutions and refine them based on customer feedback. Learn more about Amazon Connect here: https://aws.amazon.com/connect/ About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? 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. Inclusive Team Culture 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 (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth 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. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, WA, Seattle
Amazon’s Global Media and Entertainment (GME) organization is creating a future of entertainment where creative content, innovation, and commerce come together. We leverage Amazon’s unique expertise across video, music, gaming, and more to create a truly immersive entertainment experience. Our team, GME Science, is focused on building science tools to optimize Amazon’s entertainment offerings, so that we can provide a great customer experience while operating as a sustainable and profitable business. We push ourselves to Think Big, building ambitious models that create value in multiple GME businesses. This role will expand our team’s measurement work. Business leaders need to quickly understand the long-term impact of various investments, such as new website features, content creation, or marketing campaigns. Our team figures out how to take short-term signals – such as clicks or signups – and turn them into estimates of long-term financial impacts. We work with measurement teams in each business as well as central teams to build foundational measurement science and adapt it for unique use cases. One particular application for this role is to build a principled approach to valuing content/talent deals that include multiple GME businesses. Each deal is unique, featuring talent from film, sports, music, and other top industries, with contract terms that could include video content, podcasts, live appearances, and more. Our valuations need to be structured so that they are comparable across deals, yet flexible enough to account for diverse contracts. To be successful in this role, you will need effective communication, an ability to work closely with stakeholders across our many GME partner teams, and the skill to translate data-driven findings into actionable insights. This includes developing a deep understanding of our business context, which is ambiguous and can change quickly. Your work will be used by decision-makers across GME to deliver the best entertainment experience for our customers, which means we have a high bar. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help GME leaders ask the right questions, and then deliver data-driven answers, creating the future of GME at Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across GME businesses • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews
GB, Cambridge
The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications.
US, WA, Seattle
Amazons Price Optimization science team is seeking a Senior Scientist to harness planet scale multi-modal datasets, navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. This is a high visibility, high impact role for a seasoned, intellectually curious scientist able to partition customer problems into discrete solvable components, build or leverage existing approaches to deliver those components, and innovate to deploy the science into measurable customer-improving outputs. This role requires an individual with exceptional machine learning and reinforcement learning modeling expertise, a strong statistical background, excellent cross-functional collaboration skills, outstanding business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. Key job responsibilities We are hiring a senior applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: * Using cross-ASIN signals to optimally price bundles, ensure price rationality across products, and discovering and launch optimal promotional bundles * invent and deliver price optimization, simulation, and competitiveness tools for 3p Sellers. * shape and extend our bandit optimization platform - a pricing centric multi-armed bandit platform that automates the optimization of various system parameters and price inputs. * Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) About the team The Pricing Optimization science team owns price quality, discovery and discount optimization initiatives across Amazon’s internal pricing architecture as well as upwards into the customer discovery funnel. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team
US, WA, Bellevue
Amazon’s Last Mile Team is looking for a passionate individual with strong optimization and analytical skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world. Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience. Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization. Our research has direct impact on customer experience, driver and station associate experience, Delivery Service Partner (DSP)’s success and the sustainable growth of Amazon. Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting. Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities Candidates will be responsible for developing solutions to better manage and optimize delivery capacity in the last mile network. The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning. These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. 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.
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Applied Science Manager to lead a team to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. A Manager, Applied Science will be a tech leader for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead and manage the science driven solution development including design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists as well as stakeholder from different functional areas (e.g. product, engineering) on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.