The Senior Data Scientist will be a key player in AWS’s Generative AI Innovation Center (GenAIIC), a recently launched initiative aimed at helping AWS customers leverage generative AI for transformative business solutions. This role demands a blend of advanced technical expertise, customer-facing skills, and the ability to drive innovation in a fast-paced environment.

Key Responsibilities

1. Innovative AI Solutions. Develop and refine state-of-the-art generative AI algorithms to solve novel and complex problems. Work on projects that push the boundaries of what is possible with AI.
2. Customer Collaboration. Engage with clients to understand their unique business challenges and tailor generative AI solutions to meet their needs. Provide strategic guidance and support throughout the implementation process.
3. Knowledge Sharing. Create and disseminate best practices through a variety of mediums, including tutorials, blog posts, sample code, and presentations. Tailor content for technical teams as well as business and executive stakeholders.
4. Feedback Integration. Collect and analyze customer and market feedback to help guide the development of AWS products and services. Ensure that solutions align with customer needs and market trends.

About the Team

The GenAIIC team is composed of experts in AI/ML, engineering, and solutions architecture. They work closely with customers to identify impactful use cases, develop proof-of-concept projects, and scale solutions for production. The team emphasizes the responsible and efficient application of generative AI technologies, ensuring that innovations are both ethical and cost-effective.

Work Environment

1. Fast-Paced & Collaborative. The role involves working in a dynamic and collaborative environment where cross-functional teams come together to tackle challenging problems.
2. Customer-Centric. Focus on building strong relationships with customers, understanding their needs, and delivering solutions that drive significant business value.
3. Innovative Culture. AWS values continuous learning and innovation, encouraging team members to explore new ideas and technologies.

Qualifications

1. Education. Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
2. Experience. Extensive experience in developing large-scale machine learning or deep learning models. Proficiency in data querying languages (e.g., SQL), scripting languages (e.g., Python), and statistical/mathematical software (e.g., R, SAS).
3. Technical Skills. Solid understanding of algorithms, data structures, numerical optimization, data mining, and deep learning techniques. Hands-on experience with frameworks such as TensorFlow, Keras, PyTorch, or MXNet.
4. Communication Skills. Ability to convey complex technical information to diverse audiences, including both technical experts and non-technical stakeholders.

Preferred

• Advanced Degree. PhD or Master’s degree in a highly quantitative field.
• Applied Experience. Demonstrated ability to solve complex problems in practical settings.
• Deep Learning Expertise. Experience with various deep learning architectures (e.g., CNNs, RNNs, LSTMs, Transformers) and training/fine-tuning Large Language Models (LLMs).
• AWS Knowledge. Familiarity with AWS services and tools.

How to Position Yourself for This Role

1. Highlight Relevant Experience. Emphasize your experience with generative AI and large-scale model development. Showcase specific projects where you’ve applied advanced machine learning techniques to solve complex problems.
2. Demonstrate Customer Impact. Provide examples of how you’ve worked directly with clients to implement AI solutions and drive business value. Highlight any experience with customer-facing roles or projects.
3. Showcase Communication Skills. Illustrate your ability to communicate complex technical concepts to various stakeholders. Include examples of presentations, tutorials, or written content you’ve created.
4. Align with AWS Culture. Reflect your alignment with AWS’s values of innovation, inclusion, and customer focus. Mention any relevant experience working in dynamic, fast-paced environments or contributing to diverse, inclusive teams.
5. Prepare for Technical Interviews. Be ready to discuss your experience with deep learning frameworks, generative AI algorithms, and large-scale model training. Practice explaining your work in a way that is accessible to both technical and non-technical audiences.

Why AWS?

• Innovation Leadership. AWS is a leader in cloud technology and AI innovation, offering you the chance to work on groundbreaking projects.
• Inclusive Culture. AWS is committed to creating an inclusive environment where diverse perspectives are valued and celebrated.
• Career Growth. Benefit from extensive opportunities for professional development and mentorship.
• Work-Life Balance. Enjoy a supportive work environment that promotes a healthy balance between professional and personal life.