Introduction
As a Data Scientist at IBM, you will help transform our clients' data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it's investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.

Your Role and Responsibilities
As a Data Scientist for Data Platform, you will be responsible to build, analyze and improve the AI solutions that are created as Foundational assets for CIO TPT Organization.
Key Responsibilities:-

• Collaborate with business leaders, engineers, and analysts to define and prioritize data-related opportunities.
• Work closely with data engineers to ensure data availability, quality, and integration for data science initiatives.
• Apply advanced statistical methods, machine learning algorithms, and predictive analytics to uncover insights from large datasets.
• Lead the design, development, and implementation of data science models and solutions for various IT projects.
• Build and maintain scalable data pipelines and machine learning models in production environments.
• Continuously explore emerging technologies and methodologies in AI, machine learning, and data science to accelerate IBM CIO transformation.

• 4+ years of experience as a Data Scientist, preferably within an IT or technology-driven environment.
• Proficiency in programming languages such as Python, or R.
• Proficiency in Machine Learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), LLMs (e.g. IBM Granite, IBM Slate, Llama), and NLP/GenAI tools (e.g., HuggingFace, LangChain).
• Experience with generative AI techniques to develop innovative solutions for enterprise industry use cases.
• Strong experience with big data framework such as Spark.
• Experience with Iceberg: a high-performance format for huge analytic tables.
• Experience in SQL and working with relational databases.
• Proven ability to translate business challenges into data science solutions.
• Excellent communication skills and the ability to work effectively with both technical and non-technical stakeholders.

• Experience with cloud platforms for data science projects.
• Familiarity with DevOps practices, CI/CD pipelines, and MLOps tools.
• Previous experience in large-scale enterprise IT environments.