Data Scientist

Introduction

A career in IBM Software means you’ll be part of a team that transforms our customer’s challenges into solutions.Seeking new possibilities and always staying curious, we are a team dedicated to creating the world’s leading AI-powered, cloud-native software solutions for our customers. Our renowned legacy creates endless global opportunities for our IBMers, so the door is always open for those who want to grow their career.IBM’s product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive

Your Role And Responsibilities

The Data Scientist role requires a highly analytical individual proficient in Python programming, database management, and data science methodologies. You’ll focus on extracting insights from data, developing and implementing machine learning models, managing big data infrastructure, and supporting AI-driven product development.

Key Responsibilities

• Data Collection and Cleansing: Collect and cleanse data from diverse sources to ensure high-quality datasets for decision-making.
• Data Exploration and Visualization: Explore and visualize data using advanced techniques to uncover insights and trends.
• Statistical Analysis: Apply statistical and mathematical techniques to provide robust analytical foundations for predictive modeling.
• Machine Learning and Deep Learning: Develop and implement machine learning and deep learning models to address business challenges.
• ML-Ops / AI-Ops: Demonstrate expertise in ML-Ops / AI-Ops practices to ensure efficient model deployment and management.
• Big Data Management: Manage big data infrastructure and execute data engineering tasks for efficient data processing.
• Version Control and Collaboration: Utilize version control systems like Git for maintaining codebase integrity and fostering collaboration.
• AI-Driven Product Development: Design, create, and support AI-driven products to deliver impactful solutions aligned with user needs and business objectives.
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Industry Experience: Minimum of 5 years of experience in the IT industry, with a focus on data science and AI, specifically in implementing data-driven solutions
• Technical Proficiency: Proficient in Python programming, NLP techniques, and AI Frameworks (e.g., Hugging Face). Knowledge of common machine learning algorithms and frameworks: linear regression, decision trees, random forests, gradient boosting (e.g., XGBoost, LightGBM), neural networks, and deep learning frameworks such as TensorFlow and PyTorch
• Database Management: Knowledge of SQL and NoSQL database management.
• Data Science Skills: Strong background in data science, statistics, mathematics, and analytical techniques.
• Machine Learning Expertise: Expertise in machine learning and deep learning methodologies, including foundation models.
  

Preferred Technical And Professional Experience

• Deep Learning Experience: Hands-on experience in data science for 5+ years with a minimum of 4 years in deep learning.
• Big Data Technologies: Familiarity with big data technologies and data engineering practices. Familiarity with MLOps practices and tools (e.g., MLflow, Airflow).
• Cloud Computing Experience: Experience with cloud computing platforms (AWS/Azure/Google/IBM) for leveraging advanced cloud-based services.