GDS Consulting_Data Science Manager

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Job Description: EY GDS – Data and Analytics (D&A) – Manager– 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 TensorFlow or PyTorch.
•    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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