Data Scientist-Artificial Intelligence

Introduction

A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio, including Software and Red Hat.Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in groundbreaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment

Your Role And Responsibilities

Role Overview

We are seeking a Machine Learning Data Scientist with hands-on experience in building, training, and deploying ML models that solve real-world business problems. The ideal candidate should be proficient in ML algorithms, feature engineering, model evaluation, and deployment, with strong programming and statistical skills.

Preferred Education

Bachelor's Degree

Required Technical And Professional Expertise

Key Responsibilities

• Work with stakeholders to understand business problems and translate them into ML solutions.
• Perform data exploration, cleaning, and feature engineering for structured and unstructured datasets.
• Build and validate models using algorithms for classification, regression, clustering, recommendation, NLP, or CV as applicable.
• Use Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) for ML workflows.
• Deploy models into production via ML pipelines (MLflow, SageMaker, Azure ML, or equivalent).
• Track and evaluate models using MLOps practices (versioning, reproducibility, monitoring).
• Present insights and recommendations to business teams with visualizations and storytelling.
  
  

Preferred Technical And Professional Experience

Mandatory Skills

• Strong proficiency in Python for ML.
• Good understanding of statistics, probability, and hypothesis testing.
• Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch).
• Hands-on with data wrangling, feature engineering, and preprocessing.
• Practical experience with at least one cloud ML platform (Azure ML, AWS SageMaker, or GCP Vertex AI).
• Experience with model deployment and lifecycle management (MLflow or similar).
  
  

Good to Have

• Experience with NLP (transformers, embeddings) or Computer Vision (CNNs).
• Exposure to Databricks MLflow & Feature Store.
• Familiarity with big data frameworks (Spark, Databricks, Hadoop).
• Experience with real-time ML inference pipelines.