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Data Scientist/Digitalization Engineer - System Standard Parts

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Onsite

Data Scientist/Digitalization Engineer - System Standard Parts

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Data Scientist/Digitalization Engineer - System Standard Parts

Skills

Google Cloud PlatformData EngineeringPythonData ScienceKnowledge ManagementPyTorchTensorFlowMachine LearningAINLP / Natural Language ProcessingDigital TransformationJavascript

25 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

26 September 2026

What they need


At Airbus, our goal is to "pioneer sustainable aviation for a safe and united world". To achieve this, the System Standard Parts team is accelerating the digital transformation of our engineering cycle.

We are looking for a Digitalization Engineer to lead the digital evolution of our System Standard Parts qualification processes. You will be the bridge between the current qualification processes, WoW and the digital world.

You will deeply integrate Artificial Intelligence into our daily Ways of Working, focusing on key strategic pillars: optimizing design processes through advanced modeling, accelerating task execution via intelligent knowledge assistants, and enhancing VV efficiency to ensure right first time outcomes.

Responsibilities

  • The candidate will drive the application of data science across the domain, focusing heavily on leveraging unstructured data (technical reports, specifications, and documentation).
  • Generative AI NLP Solutions: Develop and implement innovative solutions using Large Language Models (LLMs) for critical engineering tasks, including document classification, automated text analysis, and efficient information retrieval from vast technical libraries.
  • Model Evaluation Assurance: Conduct thorough evaluations and statistical analysis of LLMs and other machine learning models to ensure they meet high standards of performance, reliability, and relevance to engineering requirements (Responsible AI).
  • Insight Generation Visualization: Create rapid, impactful visualizations and demos to effectively communicate complex data findings and model outputs to engineering stakeholders.
  • Prompt Engineering Workflow: Skillfully craft and refine prompts to maximize the effectiveness and quality of LLM outputs for technical use cases.
  • Upskilling Strategy: Champion continuous professional growth by leveraging the learning ecosystems. Actively participate in the communities to exchange best practices, share knowledge, and foster a culture of collective upskilling in alignment with the broader departmental and Airbus digital strategy.
  • Knowledge Management: Support team to create/manage Knowledge database and align with Airbus KM goals.
  • Stakeholder Engagement: Work closely with cross-functional teams (eg, Design, Certification, Manufacturing) and business stakeholders to understand project requirements and provide analytical support.

Who We Are Looking For

  • Preferably possess a Bachelors or Masters degree in Computer Science.
  • 5-8 years of professional experience in digitalization or engineering data science.
  • Experience in Design of Experiments (DoE) and statistical software (eg, JMP, Minitab, or Python-based libraries).
  • Proven track record in developing and deploying Machine Learning models in a physical engineering or manufacturing context.
  • Proficiency in Python (SciPy, Scikit-learn, TensorFlow/PyTorch) and JavaScript/Apps Script.
  • Capability in building enterprise-level solutions with Google AppSheet and managing Google Cloud/Workspace integrations.
  • You demonstrate networking skills to collaborate with universities, research centers and external partners.
  • Strong communication skills.
  • Candidates who demonstrate prior exposure in Electrical / Mechanical / Aerospace Systems Engineering will have an added advantage

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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