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How an Airbus data scientist keeps himself up to date

Source:Hannover Messe Release Date:2025-07-04 33
Intelligent AutomationArtificial Intelligence & Machine Learning
Tom Zehle, Data Scientist at Airbus, understands that developing each prototype demands significant time—reading research papers, deciphering code, implementing solutions, and persuading stakeholders. His objective isn’t to consume everything but to quickly identify what’s truly valuable. He shared his approach with us.

The 3-step system for the paper check

Step 1 - Screening (≈ 5 min)

Skim abstract, LinkedIn post or hugging face-readme

 

  • Alarm bells: Huge “SOTA jumps” without clear justification
  • Discrepancy with the research consensus
  • Lack of code or data

Stage 2 - Validate (≈ 15 min)

Figures + experiments + related work of a follow-up paper

 

  • Does the paper really compare its method fairly?
  • Have independent authors confirmed the results?
  • Does the data set match my own?

Stage 3 - In-depth (1-2 h)

Study core chapter, roughly execute code

 

  • How clean is the implementation?
  • Are hyperparameters properly documented?
  • Can I integrate this into my pipeline (MLOps)?

Only those who pass stage 3 end up in Tom's roadmap.

Tom uses the following tools:

 

  • Perplexity AI (with ArXiv filter): Search queries in natural language, finds papers far away from Google page 1
  • ChatGPT / Notebook LM: Get explanations, generate quiz questions, answers always with sources
  • auto-sklearn: Quickly generate baseline models and discover weak points in the data set
  • YouTube: Visual deep dives or beginner explanations
  • Reddit r/MachineLearning: Early warning system for brand new models, repos and leaks
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