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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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