How an Airbus data scientist keeps himself up to date

Source：Hannover Messe Release Date：2025-07-04 112

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