Amazon
Data Scientist, AWS, Industries
Amazon, Austin, Texas, us, 78716
Job ID: 2815262 | Amazon Web Services, Inc.Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting-edge Generative AI algorithms to solve real-world problems with significant impact? The AWS Industries Team at AWS helps customers implement Generative AI solutions and realize transformational business opportunities in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI.The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select, train, and fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost-efficiently.You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.Key Job Responsibilities
As a Data Scientist, you will:Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges.Interact with customers directly to understand the business problem, help them in the implementation of generative AI solutions, deliver briefing and deep dive sessions, and guide customers on adoption patterns and paths to production.Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders.Provide customer and market feedback to Product and Engineering teams to help define product direction.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 we strive for flexibility as part of our working 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.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.BASIC QUALIFICATIONS
5+ years of data scientist experience.3+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab, etc.).3+ years of experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.Experience applying theoretical models in an applied environment.Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.PREFERRED QUALIFICATIONS
PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.5+ years of experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer).Prior experience in training and fine-tuning of Large Language Models (LLMs).Knowledge of AWS platform and tools or equivalent cloud experience.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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As a Data Scientist, you will:Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges.Interact with customers directly to understand the business problem, help them in the implementation of generative AI solutions, deliver briefing and deep dive sessions, and guide customers on adoption patterns and paths to production.Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders.Provide customer and market feedback to Product and Engineering teams to help define product direction.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 we strive for flexibility as part of our working 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.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.BASIC QUALIFICATIONS
5+ years of data scientist experience.3+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab, etc.).3+ years of experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.Experience applying theoretical models in an applied environment.Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.PREFERRED QUALIFICATIONS
PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.5+ years of experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer).Prior experience in training and fine-tuning of Large Language Models (LLMs).Knowledge of AWS platform and tools or equivalent cloud experience.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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