About the Role

As an Associate Data Scientist at IBM, you will leverage advanced statistical, machine learning, and AI techniques to solve complex business problems. Using open-source tools such as Python, R, TensorFlow, and IBM’s AI application suites, you will analyze and interpret data, develop predictive models, and deliver actionable insights to stakeholders. This role involves working in a collaborative environment and applying analytical rigor to support business decision-making.

Roles and Responsibilities

Implement and validate predictive and prescriptive models, focusing on big data and machine learning techniques.
Create, maintain, and optimize statistical and analytical models to support business requirements.
Write programs to cleanse, integrate, and process data efficiently in reusable ways.
Work in an Agile and collaborative environment with scientists, engineers, consultants, and database administrators.
Partner with clients to understand business needs and recommend suitable modeling and analytical solutions.
Evaluate model performance and communicate results effectively to both technical and non-technical stakeholders.
Develop Proof of Concepts (POCs) to validate AI solutions, including Gen AI assistants for code transformation (e.g., COBOL to Java).
Document solution designs, architectures, implementation details, and lessons learned.
Create technical documentation, white papers, and best-practice guides.

Qualification Required

Education:

Required: Bachelor’s Degree
Preferred: Master’s Degree

Required Technical and Professional Expertise:

Hands-on experience in developing POCs for AI/ML solutions.
Strong programming skills in Python.
Experience with AI frameworks (TensorFlow, PyTorch, Keras, Hugging Face).
Proficiency in using libraries such as SciKit Learn, Pandas, Matplotlib.
Ability to collaborate with development teams and implement rapid prototypes aligned with customer requirements.
Knowledge of documenting solution architectures and best practices.

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