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Data Scientist

IBM

Cologne, North Rhine-Westphalia, GermanyOn Site

Original Job Summary

Introduction

A career in IBM Consulting is rooted in long-term relationships and global collaboration. Work with visionaries to enhance hybrid cloud and AI journeys for innovative companies using IBM and Red Hat technologies.

Your Role And Responsibilities

As a Data Scientist at IBM, you will address business challenges using open-source and IBM tools. Responsibilities include data analysis, model development, and building scalable machine learning pipelines.

  • Analyze and prepare complex data sources.
  • Implement and validate predictive and prescriptive models.
  • Develop scalable data and machine learning pipelines.
  • Utilize traditional ML and modern deep learning techniques.
  • Document and communicate results to technical and non-technical audiences.

Preferred Education & Experience

Master's Degree with strong fundamentals in Mathematics and Computer Science. Proficiency in Python, R, SQL, and relevant data science libraries is required, with experience in AI, ML, and cloud technologies considered a plus.

Key skills/competency

  • Data Analysis
  • Machine Learning
  • Predictive Modeling
  • Python
  • R
  • Big Data
  • Deep Learning
  • AI Integration
  • Cloud Technology
  • Statistics

How to Get Hired at IBM

🎯 Tips for Getting Hired

  • Research IBM's culture: Understand their mission and innovation focus.
  • Customize your resume: Highlight AI and ML project experience.
  • Showcase technical skills: Emphasize Python, R, and data analytics.
  • Prepare for interviews: Practice data science and problem-solving scenarios.

📝 Interview Preparation Advice

Technical Preparation

Review Python and R libraries.
Practice predictive and prescriptive modeling.
Test machine learning pipeline development.
Familiarize with cloud deployment tools.

Behavioral Questions

Describe a challenging project collaboration.
Explain your approach to problem solving.
Share an experience with rapid learning.
Discuss adapting to changing project scopes.