Senior Data Scientist
About the role
The Senior Data Scientist role involves designing and implementing advanced AI solutions, particularly in the areas of NLP, Generative AI, LLMs, and MLOps. The position requires 3-7 years of experience in Data Science and Machine Learning, with responsibilities including developing AI models, integrating with APIs and libraries, optimizing data pipelines, and ensuring compliance with data privacy and ethical standards. Candidates should have strong programming skills, experience with cloud platforms, and a solid understanding of machine learning and deep learning techniques. The role is positioned within a team focused on leveraging AI for enterprise industry use cases, with opportunities to collaborate with stakeholders and domain experts.
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 send their resume to anusri.arulchelvam@gds.ey.com. A Ph.D. is considered a plus but not required. The listing does not specify location, remote work, or salary details.
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