Data Scientist, Generative AI Innovation Center

Data Scientist, Generative AI Innovation Center

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 Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI.

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.

We're looking for ML Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Key Job Responsibilities

As an ML Data Scientist, you will:

1. Collaborate with ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges.
2. Interact with customers directly to understand the business problem, help and aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths to production.
3. Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholders.
4. Provide customer and market feedback to Product and Engineering teams to help define product direction.

About the Team

The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

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.

Minimum Qualifications

- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.
- Relevant experience in building large scale machine learning or deep learning models.
- Experience communicating across technical and non-technical audiences.
- Experience in using Python and hands-on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet.
- Fluency in written and spoken English.

Preferred Qualifications

- Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue).
- PhD or Master's degree in Computer Science, or related technical, math, or scientific field.
- Proven knowledge of Generative AI and hands-on experience of building applications with large foundation models.
- Proven knowledge of AWS platform and tools.
- Hands-on experience of building ML solutions on AWS.
- High impact thought leadership in AI/ML space through blog posts, public presentations, social media visibility, or publications.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build.

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.