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EY GDS – Data and Analytics (D&A) – Manager – Data Scientist

Role Overview:

We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 8 - 11 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role.

Responsibilities:

Your technical responsibilities:

• Contribute to the design and implementation of state-of-the-art AI solutions.

• Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.

• Collaborate with stakeholders to identify business opportunities and define AI project goals.

• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.

• Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.

• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.

• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.

• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.

• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.

• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.

• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.

• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.

• Ensure compliance with data privacy, security, and ethical considerations in AI applications.

• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus.

• Minimum 8-11 years of experience in Data Science and Machine Learning.

• In-depth knowledge of machine learning, deep learning, and generative AI techniques.

• Proficiency in programming languages such as Python, R, and frameworks like Tensor Flow or Py Torch.

• Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.

• Familiarity with computer vision techniques for image recognition, object detection, or image generation.

• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.

• Expertise in data engineering, including data curation, cleaning, and preprocessing.

• Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.

• Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models.

• Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.

• Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels.

• Understanding of data privacy, security, and ethical considerations in AI applications.

• Track record of driving innovation and staying updated with the latest AI research and advancements.

Good to Have Skills:

• Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems.

• Utilize optimization tools and techniques, including MIP (Mixed Integer Programming).

• Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models.

• Implement CI/CD pipelines for streamlined model deployment and scaling processes.

• Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.

• Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation.

• Implement monitoring and logging tools to ensure AI model performance and reliability.

• Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment.

• Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.

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