Careers

The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in quantum computing for the benefit of our customers. We are looking to hire an Applied Scientist to design and model novel superconducting quantum devices (including qubits), readout and control schemes, and advanced quantum processors. The ideal candidate will have a track record of original scientific contributions, strong engineering principles, and/or software development experience. Resourcefulness, as well as strong organizational and communication skills, is essential. About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, 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 conferences, inspire us to never stop embracing our uniqueness. 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. 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. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a U.S export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.

This role sits in Business Innovation and AI (BIA), part of Global Ops Enterprise Services (GOES), and works on the Real Estate Optimization System (REOS). REOS decides where Amazon builds. It models the supply chain from fulfillment centers through sort centers and delivery stations to the customer as a time-space network over a 20-year horizon, and ranks candidate sites on total network cost — transportation, labor, real estate and unmet demand — rather than on rent alone. Alongside the core engine we are building a family of AI tools that source candidate properties from across the market, assemble and validate the data behind a study, and generate the cost briefs that explain a recommendation, and we are extending the same optimization to zip-code-level Sub Same Day siting. You will work alongside scientists, and with partner teams in GOES Portfolio Management, Capacity Planning and Finance. We are looking for an Applied Scientist to own the optimization models behind REOS. You will formulate the models that decide where Amazon builds, engineer the solution methods that make them tractable at network scale, lead the initiatives that extend them, and take the recommendations to the Development Managers and Finance partners who act on them. The work is hands-on and close to the decision: your models become the basis for real capital commitments. The people who make those commitments are your customers. You will work with Development Managers, Capacity Planning teams and Finance partners to understand what they need, build and run the model that answers it, deliver the result, and explain what it recommends in terms they can act on and defend. Reporting to the Science Manager, you will own the technical approach on the models you build and mentor the scientists working alongside you. Key job responsibilities • Formulate and solve large-scale optimization models — linear, mixed-integer, network flow and stochastic or robust variants — that recommend site locations and network topology at lowest total cost. • Select and engineer the solution methodology — decomposition, column generation, cutting planes, relaxations and heuristics — so models solve reliably at production scale and within business timelines. • Own the modeling assumptions and the cost structure behind them, and make the trade-offs explicit and defensible to Finance. • Implement and productionize models in Python against commercial and open-source solvers, with the data pipelines and validation that keep them trustworthy, and maintain them as the network and the data change. • Engage customers directly: elicit the real problem, translate it into a formulation, run scenarios with them, deliver results, and explain what the model recommends and why. • Lead initiatives end to end, apply AI tooling to the science workflow, and raise the bar for the scientists around you through design review and mentoring. A day in the life You might start your morning reviewing experiment results and refining a machine learning model before syncing with engineers on a production integration plan. After lunch, you could be whiteboarding a new algorithmic approach with colleagues from product and science teams, then drafting a research document that outlines your methodology and findings. You will regularly present your work to leadership, participate in science reviews, and stay connected to the latest research in your field to inform your next initiative. Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skill sets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Our team is focused on applying advanced science to solve high-impact problems that improve Amazon's products and customer experience. We value collaboration, intellectual curiosity, and a commitment to scientific rigor. You will work alongside researchers, engineers, and product managers in an inclusive environment where new ideas are encouraged and your contributions directly influence the direction of our roadmap. If you are looking for a place where research translates into real outcomes, we would love to hear from you.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? If so - come and work with us, and you will define the next generation of how and what Amazon customers are watching. Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at any time and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on We are seeking an exceptional Applied Scientist to join our Prime Video Sports personalization team in Israel. Our team is dedicated to developing state-of-the-art science to personalize the customer experience and help customers seamlessly find any live event in our selection. You will have the opportunity to work on innovative, large-scale projects that push the boundaries of what's possible in sports content delivery and engagement. Your expertise will be crucial in tackling complex challenges such as information retrieval, sequential modeling, realtime model optimizations, utilizing Large Language Models (LLMs), and building state-of-the-art complex recommender systems. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Personalization, Information Retrieval, and Recommender Systems, or general ML to develop new algorithms and end-to-end solutions. As part of our team of applied scientists and software development engineers, you will be responsible for researching, designing, developing, and deploying algorithms into production pipelines. Your role will involve working with the latest technologies in recommender systems and search. You'll also tackle unique challenges like temporal information retrieval to improve real-time sports content recommendations. As a technologist, you will drive the publication of original work in top-tier conferences in Machine Learning and Recommender Systems. We expect you to thrive in ambiguous situations, demonstrating proven analytical abilities and comfort in collaborating with cross-functional teams and systems. The ideal candidate is a self-starter with the ability to learn and adapt quickly in our fast-paced environment. About the team We are the Prime Video Sports team. In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis majors like Roland-Garros and English Premier League to list a few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.

Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at any time and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on We are seeking an exceptional Applied Scientist to join our Prime Video Sports personalization team in Israel. Our team is dedicated to developing state-of-the-art science to personalize the customer experience and help customers seamlessly find any live event in our selection. You will have the opportunity to work on innovative, large-scale projects that push the boundaries of what's possible in sports content delivery and engagement. Your expertise will be crucial in tackling complex challenges such as information retrieval, sequential modeling, real-time model optimization, utilizing Large Language Models (LLMs), and building advanced recommender systems. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Personalization, Information Retrieval, and Recommender Systems — or strong general ML fundamentals — to develop new algorithms and end-to-end solutions. As part of our team of applied scientists and software development engineers, you will research, design, develop, and deploy algorithms into production pipelines. You'll work with state-of-the-art technologies in recommender systems and search, and tackle unique challenges like temporal information retrieval to improve real-time sports content recommendations. As a technologist, you will also drive the publication of original work in top-tier Machine Learning and Recommender Systems venues. About the team We're the Sports Science team behind personalization for live sports on Prime Video. We build custom deep learning models that predict what each customer wants to watch across live and upcoming events -- powering relevance and ranking at scale for one of the most dynamic, real-time personalization problems at Amazon. Our work spans the full stack: engineering, Transformer-based architectures, calibration, and low-latency serving in production. We own our models end to end, from research to deployment. If you're excited about applied science that ships and reaches millions of customers, come build with us.

Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalised, and effective experience. Alexa Sensitive Content Intelligence (ASCI) team is developing responsible AI (RAI) solutions for Alexa+, empowering it to provide useful information responsibly. The Mission Build AI safety systems that protect millions of Alexa customers every day. As conversational AI evolves, you'll solve challenging problems in Responsible AI by ensuring LLMs provide safe, trustworthy responses, building AI systems that understand nuanced human values across cultures, and maintaining customer trust at scale. What You'll Build You'll pioneer breakthrough solutions in Responsible AI at Amazon's scale. Imagine training models that set new safety standards, designing automated testing systems that hunt for vulnerabilities before they surface, and certifying the systems that power millions of daily conversations. You'll create intelligent evaluation systems that judge responses with human-level insight, build models that truly understand what makes interactions safe and delightful, and craft feedback mechanisms that help Alexa+ grasp the nuances of complex customer conversations. Here's where it gets even more exciting: you'll build AI agents that act as your team's safety net—automatically detecting and fixing production issues in real-time, often before anyone notices there was a problem. Your innovations won't just improve Alexa+; they'll fundamentally shape how it learns, evolves, and earns customer trust. As Alexa+ continues to delight customers, your work ensures it becomes more trustworthy, safer, and deeply aligned with customer needs and expectations. Your work directly protects customer trust at Amazon's scale. Every innovation you create—from novel safety mechanisms to sophisticated evaluation techniques—shapes how millions of people interact with AI confidently. You're not just building products; you're defining industry standards for responsible AI. This is frontier research with immediate real-world impact. You'll tackle problems that require innovative solutions: training models that remain truthful and grounded across diverse contexts, building reward models that capture the nuanced spectrum of human values across cultures and languages, and creating automated systems that continuously discover and address potential issues before customers encounter them. You'll collaborate with world-class scientists, product managers, and engineers to transform state-of-the-art ideas into production systems serving millions. What We're Looking For * Deep expertise in state-of-the-art NLP, Large Language Models * Track record of building scalable ML systems * Passion for impactful research—where frontier science meets real-world responsibility at scale * Excitement about solving problems that will shape the future of AI Ready to work on AI safety challenges that define the industry? Join us. Key job responsibilities The Responsible AI (RAI) team is at the forefront of building AI systems that are safe, ethical, and trustworthy. As an Applied Scientist-II, you'll be a key contributor to end-to-end Responsible AI solutions—developing and refining algorithms that detect and eliminate false information, building models that identify vulnerabilities exploited by bad actors, and training systems that understand human values across diverse cultures. Working alongside world-class engineers and scientists, you'll apply state-of-the-art NLP and computer vision techniques to content classification and safety challenges—turning cutting-edge research into production systems that protect millions of customers daily. But here's what makes this role exciting: you'll grow into a future leader. You'll contribute to identifying, defining, and mitigating AI safety risks early—helping protect customers before they're impacted—while building deep expertise in responsible AI. You'll work within multi-agent architectures, implementing components where distributed AI systems maintain human oversight and control. Every model you build and every experiment you run—from consensus-driven reasoning modules to orchestration components—will directly strengthen trustworthy AI at the world's largest scale. A day in the life As a Responsible AI Scientist, you're at the frontier of AI safety—experimenting with breakthrough techniques that push the boundaries of what's possible. You partner with engineering to transform research into production-ready solutions, tackling complex optimization challenges. You brainstorm with Product teams, translating ambitious visions into concrete objectives that drive real impact. Your expertise shapes critical deployment decisions as you review impactful work and guide go/no-go calls. You mentor the next generation of AI safety leaders, watching ideas spark and capabilities grow. This is where science meets impact—building AI that's not just intelligent, but trustworthy and aligned with human values. About the team Our team pioneers Responsible AI for conversational assistants. We ensure Alexa delivers safe, trustworthy experiences across all devices, modalities, and languages worldwide. We work on frontier AI safety challenges—and we're looking for scientists who want to help shape the future of trustworthy AI. 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.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist II Job Location: Culver City, California Job Number: AMZ10564056 Position Responsibilities: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. 40 hours / week, 8:00am-5:00pm, Salary Range: $158,681/year to $184,000/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

At Amazon, we are committed to being the Earth’s most customer-centric company. The European International Technology group (EU INTech) owns the enhancement and delivery of Amazon’s engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects. You will be joining the Tools and Machine Learning (Tamale) team. As part of EU INTech, we tackle complex, high-impact problems at the intersection of e-commerce, AI, and customer experience; going well beyond traditional ML pipelines. Our work spans multiple frontiers: we build GenAI-powered data solutions, enabling scalable and cost-efficient training data generation at Amazon scale. We design and deploy agentic systems for detecting and fixing quality issues across Amazon's retail surfaces. And we develop ranking and recommendation models for Haul shopping experiences. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading machine learning solutions. We strongly value your hard work and obsession to solve complex problems on behalf of Amazon customers. Key job responsibilities We look for applied scientists who possess a wide variety of skills. As the successful applicant for this role, you will with work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to automate manual processes, to scale existing systems and to improve catalog data quality, to name just a few. You will work with business leaders, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will be part of team of scientists and engineers working on solving data quality issues at scale. You will be able to influence the scientific roadmap of the team, setting the standards for scientific excellence. You will be working with state-of-the-art models, including image to text, Multimodal LLMs and GenAI. Your work will improve the experience of millions of daily customers using Amazon in Europe and in other regions. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process!

The Frontier AI Assets (FAI) team is looking for a passionate, talented, and inventive Principal Applied Scientist with a strong deep learning and generative AI background, to focus on the development of software development skills of Nova foundational models. As a Principal Applied Scientist, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification, and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and playing a significant role in hiring and developing scientists and leads. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop frontier quality assets for training and evaluating GenAI models. You will also participate in organizational planning, hiring, mentorship, and leadership development. You will be technically strong and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, and testing to documentation, delivery, and maintenance). About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers. Our team is focused on pushing the boundaries of applied science at Amazon, building solutions that operate at massive scale and directly shape customer experiences. We value scientific depth, collaborative problem-solving, and a commitment to turning research into measurable impact. We are investing in the next wave of scientific capabilities and are looking for a Principal Applied Scientist to help define that trajectory. You will work alongside talented scientists, engineers, and product partners in an environment that encourages intellectual curiosity and rewards bold ideas.

Can a large language model read a regulatory change and draft the requirements and control tests that follow from it, accurately enough for a Compliance Officer to sign off? How do you train a breach-risk model when confirmed breaches are rare, arrive months after the event, and mean different things to Compliance, Assurance, and the regulator? What does a model have to prove before a Compliance team acts on its output? We are looking for an Applied Scientist to join the Data and Analytics team within Risk and Compliance Solutions (RCS). Our team builds the data, analytics, and modeling layer that RCS runs on, from regulatory reports and filings to the intelligence that tells Compliance Officers and our first line of defense (1LoD) partners where risk concentrates and which controls hold. You build the machine learning (ML) and generative artificial intelligence (GenAI) systems in that layer, take them into production, and own the evaluation science that lets Compliance rely on them. The work spans 3 areas: risk models that score which open risks and control weaknesses convert to breaches; GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage; and the evaluation science behind every model and agent the team ships. Financial crime compliance is the first domain, and its constraints shape the science: threats change faster than control sets adapt, and every output faces internal audit and external examination. Once an approach proves out, you carry it into the next compliance domain across the RCS portfolio. You own problems from formulation to production: framing the science question with Compliance and 1LoD partners, designing experiments and backtests, building and validating models, and shipping them into the pipelines, reports, and tools that RCS teams use. The stack includes Amazon SageMaker, Amazon Bedrock, Amazon Redshift, and AWS Lambda, and your partners include data scientists, data engineers, business intelligence engineers, and software development engineers across RCS. The successful candidate treats an auditor's question as a design requirement, writes production-quality code alongside their models, and stays comfortable when the evaluation set does not exist until they build it. Key job responsibilities • Formulate ambiguous compliance problems as ML and GenAI problems with Compliance and 1LoD partners, and choose methods that fit the data, the risk, and the audit standard • Develop risk models that learn from rare, delayed, and noisy labels to score which risks and control weaknesses convert to breaches, and calibrate them so a score carries a defensible meaning • Build GenAI systems that remove manual effort from requirement translation, control testing, and regulatory reporting, with human approval gates at every stage • Design the evaluation for every ML and GenAI system you ship: golden sets that compliance experts label, metrics suited to rare outcomes and generated text, and release criteria a system meets before Compliance relies on it • Deploy and monitor models in production with software development engineers, including drift detection, performance tracking against realized outcomes, and retraining criteria • Explain model decisions, quantify uncertainty, and document methodology so Compliance Officers, auditors, and examiners can trace how a system reached each output • Create reusable science components, such as feature sets, scoring services, and evaluation harnesses, so an approach that works in financial crime compliance extends to the next domain without a rebuild • Review peer models for statistical soundness, mentor data scientists and engineers on ML methods and experimental design, and publish or patent where the work and confidentiality allow About the team Risk and Compliance Solutions (RCS) serves as a crucial safeguard for Amazon's buyers, brands, and selling partners while ensuring business teams meet their regulatory obligations. RCS operates as the second line of defense under the Three Lines of Defense (3LoD) model, providing specialized oversight, monitoring, and expertise while setting policies and procedures that guide Amazon's compliance framework. RCS's mission is to apply specialist industry insights and Amazon's unique approach to scale through technology, delighting customers through tailored compliance solutions. Our work spans multiple critical areas, including: • Streamlined license acquisition that enables Amazon's financial services expansion • Management of existing business risks across the organization • Comprehensive governance, risk, and assurance services • Innovative solutions such as Compliance as a Service (CaaS) • A proactive, intelligence-led framework for compliance operations

The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development