Data Scientist - Supply Chain Engineering

At IBM Infrastructure & Technology, we design and operate the systems that keep the world running. From high-resiliency mainframes and hybrid cloud platforms to networking, automation, and site reliability. Our teams ensure the performance, security, and scalability that clients and industries depend on every day. Working in Infrastructure & Technology means tackling complex challenges with curiosity and collaboration. You’ll work with diverse technologies and colleagues worldwide to deliver resilient, future-ready solutions that power innovation. With continuous learning, career growth, and a supportive culture, IBM provides the opportunities to build expertise and shape the infrastructure that drives progress. As a Data Scientist with expertise in Artificial Intelligence, you will skillfully combine data analysis and business acumen to tackle cognitive computing challenges. You will be responsible for architecting and delivering AI solutions using cutting-edge technologies, with a strong focus on foundation models and large language models. Your primary responsibilities will include: • Design AI Solutions: Architect and deliver AI solutions using cutting-edge technologies, with a strong focus on foundation models and large language models, and experience in tools like Github Copilot and Amazon Code Whisperer. • Develop Cognitive Solutions: Create comprehensive cognitive solutions that effectively process and analyze both structured and unstructured data, utilizing expertise in NLP, ML, and other specialized areas such as Image Processing, Video Processing, Voice Processing, or Watson technologies. • Implement AI Frameworks: Apply strong programming skills, with proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, Keras, or Hugging Face, to develop and deploy AI models. • Manage AI Project Lifecycle: Oversee the full AI project lifecycle, from research and prototyping to deployment in production environments, ensuring successful project delivery. • Collaborate with Stakeholders: Work with various stakeholders to identify business problems and leverage the power of artificial intelligence for cognitive computing, driving business value through AI-driven solutions. • Advanced Analytics Techniques: Exposure to advanced analytics techniques for structured data, including data analysis and interpretation, to inform business decisions. • AI Frameworks and Tools: Experience working with AI frameworks such as TensorFlow, PyTorch, Keras, or Hugging Face, and tools like Github Copilot and Amazon Code Whisperer. • Programming Skills: Proficiency in Python programming language, with experience in applying programming skills to develop and deploy AI models. • NLP and ML Methods: Exposure to Natural Language Processing (NLP) and Machine Learning (ML) methods for unstructured content, including foundation models and large language models. • Cloud Platforms and Databases: Experience working with cloud platforms (e.g., Kubernetes, AWS, Azure, GCP) and relational and NoSQL databases (e.g., SQL, Postgres, DB2, MongoDB). • Familiarity with Modern UI: Familiarity with modern UI frameworks such as Backbone.js, AngularJS, React.js, Ember.js, Bootstrap, and JQuery, with the ability to apply this knowledge in developing AI-driven solutions. • Understanding of Libraries: Understanding in the usage of libraries such as SciKit Learn, Pandas, Matplotlib, etc., with the ability to apply this knowledge in developing and deploying AI models. • Operating Systems Knowledge: Experience working with various operating systems, including Linux, Windows, iOS, and Android, with the ability to adapt AI solutions to different operating systems.