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Python Developer / Data Scientist

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Summary

A Python developer/data scientist will co-build an NDT (non-destructive testing) project alongside the current team, then take long-term ownership of the codebase: maintaining it, evaluating model performance, retraining models, and adapting it to business needs. Core stack is production Python, data science with MLflow, and Git, with nice-to-haves in classical ML, computer vision, Kubernetes, and

Python Developer / Data Scientist

Overview

A Python developer with a data science background to become the long-term owner of the NDT project. The developer will co-build across the entire codebase with the current team to understand it and then take over ownership once the current team leaves. From that point, the developer will keep the project running and continuously adapt it to changing business requirements.

Expected Tools & Knowledge

Must have

  • Python: solid and production-minded
  • Data science
  • Experiment tracking with MLflow
  • Collaborative programming and Git
  • Self-reliance, with the ability to unblock minor technical issues independently

Nice to have

  • Classical machine learning
  • Computer vision
  • Kubernetes
  • REST / FastAPI

Responsibilities

  • Co-build across the entire codebase with the current team and prepare to take full ownership once they leave.
  • Own the project long term, maintain it, and continuously adapt it to changing business requirements.
  • Continuously evaluate model performance.
  • Collect new data and re-trigger model training when needed.
  • Critically challenge the training data and determine whether a model is good enough to deploy.

Competencies

Python & Software Engineering

Solid, production-minded Python.

Comfortable taking over and maintaining an existing codebase.

Uses Git and works effectively in a collaborative development setup.

Data Science & Model Judgment

Strong grounding in data science.

Questions whether the training data is sound and whether a model is genuinely ready to deploy, rather than trusting metrics at face value.

Evaluates model performance and knows when to collect data and retrain.

Business Stakeholder Interaction

Works directly with business stakeholders to understand and clarify changing requirements.

Translates business requests into concrete changes to the project.

Communicates results and model limitations clearly to non-technical audiences.

Skills

See also

Data Science jobs by country — openings, pay and top skills →

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