Manager- Data Science
About the role
EY GDS is seeking a Senior Data Scientist with 3-7 years of experience in Data Science and Machine Learning, ideally with exposure to NLP, Generative AI, LLMs, MLOps, optimization, and AI solution architecture. The role involves designing and implementing advanced AI solutions, collaborating with stakeholders to define project goals, and leveraging the latest generative AI techniques for enterprise use cases. Candidates should have strong programming skills, experience with cloud platforms, and a solid understanding of data engineering and trusted AI practices. The position offers the opportunity to work on cutting-edge AI projects within a collaborative, innovative environment.
Must have
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus.
- Minimum 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 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.
Nice to have
- 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.
Technologies
- AWS
- Azure
- CI/CD
- CloudFormation
- Computer Vision
- Data Engineering
- Data Science
- Deep Learning
- DevOps
- Docker
- GCP
- Generative AI
- Git
- Hugging Face
- Infrastructure as Code
- Kubernetes
- LLM
- Machine Learning
- MLOps
- Model Deployment
- NLP
- NoSQL
- Python
- PyTorch
- Redis
- TensorFlow
- Terraform
- Transformers
- Vector Databases
Highlights
Applicants are instructed to share their resume directly to anusri.arulchelvam@gds.ey.com. A Ph.D. is considered a plus. The listing does not specify location, remote work, or visa requirements.
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