Sr. Data Scientist, Professional Services

- Master's or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
- Extensive experience (5+ years) 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.
- Excellent problem-solving, analytical, and critical thinking skills.
- Ability to communicate complex technical concepts to both technical and non-technical audiences.
- Experience with large language models, such as Claude, GPT, BERT, or T5.
- Knowledge of generative adversarial networks (GANs) and their applications in image generation or other domains.
- 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 publication record in top-tier conferences and journals in the field of generative AI or related areas.
- Excellent communication, collaboration, and leadership skills.
The Senior Data Scientist, plays a crucial role in driving the development and implementation of cutting-edge 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 strong problem-solving skills and a passion for innovation.

Key job responsibilities
1. Generative AI Model Development:
-Design and develop state-of-the-art 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.
-Monitor and maintain deployed models, ensuring their continued performance and adherence to ethical and regulatory standards.

5. Collaboration and Knowledge Sharing:
-Collaborate with data engineers, software engineers, and subject matter experts to develop innovative solutions leveraging generative AI.
-Mentor and guide junior data scientists, fostering knowledge sharing and promoting best practices in the field of generative AI.
-Contribute to the firm's thought leadership by publishing research papers, presenting at conferences, and participating in industry events.


About the team
About AWS

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