Data Scientist Artificial Intelligence

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

As a Data Scientist 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, ensuring successful project delivery.

Required Technical And Professional Expertise

• Strong Fundamental Coding Practices
  • Clear understanding of Python best practices and clean coding principles.
  • Proper variable scoping and naming conventions (no reuse of variables for different purposes within the same script).
  • Ability to write modular, readable, and maintainable code.
• Production-Grade Repository Experience
  • Experience working with repositories that transition from development/testing to production environments.
  • Familiarity with version control best practices (branching strategy, code reviews, structured commits).
  • Ability to implement production-readiness requirements (logging, error handling, unit testing, configuration management, reproducibility).
• Solid Machine Learning Development Knowledge
  • Strong understanding of core ML concepts (feature selection, feature engineering, model validation techniques).
  • Familiarity with proper validation strategies (cross-validation, train/test split methodology, avoiding data leakage).
  • Ability to explain and justify modeling decisions clearly.
  • Experience with MLOps.

Preferred Technical And Professional Experience

• Independent Problem-Solving and Initiative
  • Ability to work with high-level objectives and translate them into concrete technical improvements.
  • Strong debugging and obstacle-resolution skills without requiring step-by-step guidance.
  • Proactive mindset toward improving repository structure, performance, and maintainability.

Preferred Education

Bachelor's Degree

Key skills/competency

• Data Analysis
• Artificial Intelligence
• Machine Learning
• Foundation Models
• Large Language Models
• Python Programming
• MLOps
• Version Control
• Problem Solving
• Cognitive Computing