Data Scientist, Data Intelligence, Professional Services GCR

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

The Amazon Web Services Professional Services team is looking for a Data Scientist, this role plays a crucial role in delivering the generative artificial intelligence (GenAI) solutions for our clients. This position requires a deep understanding of machine learning, natural language processing, and generative models, combined with problem-solving skills and a passion for innovation.

Key job responsibilities

1. Generative AI Model Development:
   -Design and develop generative AI models, including language models, image generation models, and multimodal models.
   -Explore and implement advanced techniques in areas such as transformer architectures, attention mechanisms, and self-supervised learning.
   -Conduct research and stay up-to-date with the latest advancements in the field of generative AI.
   
   2. Data Acquisition and Preprocessing:
   -Identify and acquire relevant data sources for training generative AI models.
   -Develop robust data preprocessing pipelines, ensuring data quality, cleanliness, and compliance with ethical and regulatory standards.
   -Implement techniques for data augmentation, denoising, and domain adaptation to enhance model performance.
   
   3. Model Training and Optimization:
   -Design and implement efficient training pipelines for large-scale generative AI models.
   -Leverage distributed computing resources, such as GPUs and cloud platforms, for efficient model training.
   -Optimize model architectures, hyperparameters, and training strategies to achieve superior performance and generalization.
   
   4. Model Evaluation and Deployment:
   -Develop comprehensive evaluation metrics and frameworks to assess the performance, safety, and bias of generative AI models.
   -Collaborate with cross-functional teams to ensure the successful deployment and integration of generative AI models into client solutions.
   
   5. Collaboration and Knowledge Sharing:
   -Collaborate with data engineers, software engineers, and subject matter experts to develop innovative solutions leveraging generative AI.
   -Contribute to the firm's thought leadership by presenting at conferences, and participating in industry events.
   
   About the team
   AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.
   
   About AWS
   
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• Master's or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
  
• 4+ years of experience in developing and deploying machine learning models, with a strong focus on generative AI techniques.
  
• Proficiency in programming languages such as Python, PyTorch, or TensorFlow, and experience with deep learning frameworks.
  
• Strong background in natural language processing, computer vision, or multimodal learning.
  
• Ability to communicate technical concepts to both technical and non-technical audiences.
  
• Experience with large language models, such as Claude, GPT, BERT, or T5.
  
• Familiarity with reinforcement learning techniques and their applications in generative AI.
  
• Understanding of ethical AI principles, bias mitigation techniques, and responsible AI practices.
  
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and distributed computing frameworks (e.g., Apache Spark, Dask).
  
• Strong problem-solving, analytical, and critical thinking skills.
  
• Strong communication, collaboration, and leadership skills.
  
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