Data Scientist & AI ELH - RTP 2027

At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk We are seeking a motivated and technically skilled early-career professional to join our AI and Data Science development team. As a Junior Developer, you will contribute to the design, development, and implementation of AI solutions and look for ways to efficiently collect, clean, analyze, and visualize data to support business decisions that support real-world applications across enterprise systems. This role is ideal for someone with a strong foundation in machine learning and software engineering who is eager to grow in a collaborative, innovation-driven environment. You will work with Senior Developers to build models helping to create predictive models, generate insights and help optimize company performance Proficiency in Python and experience with libraries such as NumPy, pandas, scikit-learn. Solid understanding of machine learning algorithms and model evaluation techniques. Experience with Git and collaborative development workflows. Ability to work with structured and unstructured data, including preprocessing and transformation. Familiarity with software engineering principles and debugging practices. Strong analytical and problem-solving skills. Experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Keras). Exposure to model deployment using Docker, REST APIs, or cloud platforms (AWS, Azure, GCP). Understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines). Knowledge of distributed systems, storage architectures (e.g., IBM Storage Scale), and performance optimization. Familiarity with Linux environments and container orchestration (e.g., Kubernetes, OpenShift). Awareness of ethical AI principles, including fairness, transparency, and bias mitigation. United States Data & Analytics Hybrid Entry Level Multiple Cities RESEARCH TRIANGLE PARK DURHAM (0147) International Business Machines Corporation Algorithms Amazon Web Services Artificial Intelligence Debugging Deep Learning Distributed Systems Docker Git Linux Machine Learning Optimization Python Storage Azure Google Cloud Platform Kubernetes Representational State Transfer Application Programming Interface Transparency Software Engineering Keras Library NumPy Pandas Library PyTorch Scikit-learn Tensorflow Unstructured Data OpenShift Real-time Transport Protocol Musical Orchestration Kubeflow Scale Cloud Platforms Bias Mitigation Machine Learning Operations Continuous Integration/Continuous Delivery Pipelines Ethical AI MLflow IBM Storage