Job
Description
Role Overview: As a Senior Data Scientist at EY GDS Data and Analytics (D&A), you will leverage your expertise with a minimum of 3 - 7 years of experience in Data Science and Machine Learning to play a key role in developing and implementing AI solutions. Your deep understanding of AI technologies and experience in designing cutting-edge AI models will be crucial in this role. Additionally, proficiency in data engineering, DevOps, and MLOps practices will add value to your responsibilities. Responsibilities: - Contribute to the design and implementation of state-of-the-art AI solutions. - Assist in developing and implementing AI models and systems, utilizing techniques like Language Models (LLMs) and generative AI. - Collaborate with stakeholders to identify business opportunities and define AI project goals. - Stay updated with advancements in generative AI techniques, such as LLMs, and assess their potential applications in solving enterprise challenges. - Utilize generative AI techniques, like LLMs, to innovate solutions for enterprise industry use cases. - Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, for enhanced generative AI capabilities. - Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. - Utilize vector databases, like Redis, and NoSQL databases for efficient handling of large-scale generative AI datasets. - Implement similarity search algorithms for accurate retrieval of relevant information from generative AI outputs. - Collaborate with domain experts, stakeholders, and clients to tailor generative AI solutions to specific business requirements. - Research and evaluate advanced AI techniques to enhance performance and efficiency. - Establish evaluation metrics to assess the quality and relevance of generative AI outputs for enterprise industry use cases. - Ensure compliance with data privacy, security, and ethical considerations in AI applications. - Utilize data engineering skills for data curation, cleaning, and preprocessing for generative AI applications. Requirements: - Bachelor's or Master's degree in Computer Science, Engineering, or related field (Ph.D. is a plus). - 3-7 years of experience in Data Science and Machine Learning. - In-depth knowledge of machine learning, deep learning, and generative AI techniques. - Proficiency in Python, R, TensorFlow, or PyTorch. - Strong understanding of NLP techniques and frameworks like BERT, GPT, or Transformer models. - Experience with cloud platforms such as Azure, AWS, or GCP. - Expertise in data engineering, trusted AI practices, and collaboration with software engineering teams. - Excellent problem-solving skills and ability to translate business requirements into technical solutions. - Strong communication and interpersonal skills. - Understanding of data privacy, security, and ethical considerations in AI applications. - Track record of innovation and staying updated with AI research. Good to Have Skills: - Apply trusted AI practices for fairness, transparency, and accountability. - Utilize optimization tools like MIP. - Drive DevOps and MLOps practices for continuous integration and deployment. - Implement CI/CD pipelines for streamlined model deployment. - Use tools such as Docker, Kubernetes, and Git for managing AI pipelines. - Apply infrastructure as code principles with tools like Terraform or CloudFormation. - Implement monitoring and logging tools for AI model performance. - Collaborate effectively with software engineering and operations teams.,
