Big Data Scientist for the Services Analytics Plateau

Airbus
Germany
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: mastersSkills: ["Stakeholder communication","Problem-solving","Knowledge sharing","Presenting findings","Data governance"]

Develop and advance data analytics solutions for Airbus Defence and Space’s Services Analytics Plateau, supporting digitalization across large aircraft and key customer use cases. You’ll perform exploratory data analysis, data wrangling, and statistical modeling to build and validate machine learning and data mining models, including health monitoring and predictive maintenance. Work with agile teams to prototype and productionize Python-based pipelines, drive new data governance methods, and communicate findings to engineers and managers.

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FursaFursa
Airbus
Airbus
6 hours ago

Big Data Scientist for the Services Analytics Plateau

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Source: Company careers pageValidated by: Fursa AI
Last checked: 6 hours agoStatus: Live
Reposted: similar role first listed 5 months ago

Job Summary

Develop and advance data analytics solutions for Airbus Defence and Space’s Services Analytics Plateau, supporting digitalization across large aircraft and key customer use cases. You’ll perform exploratory data analysis, data wrangling, and statistical modeling to build and validate machine learning and data mining models, including health monitoring and predictive maintenance. Work with agile teams to prototype and productionize Python-based pipelines, drive new data governance methods, and communicate findings to engineers and managers.
Location: Germany
Workplace: Onsite
Employment Type: Full time · Permanent
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Lead the discovery process by asking the right questions and identifying data-analytics problems with the highest organizational opportunities.
  • •Perform exploratory data analysis, visualization, and data wrangling (transforming, cleansing, and linking data).
  • •Build and validate statistical, data mining, and machine learning models, including work involving MCMC simulations and output analysis.
  • •Develop and implement prototype and production code, primarily in Python, to deliver data sets for machine learning models.
  • •Present results to diverse audiences and collaborate closely with engineers, support teams, and design office stakeholders, including execution of proof-of-concept requests.

Pay and Benefits

Perks:PensionHealth InsurancePaid LeaveLearning Budget

Key Requirements

  • •M.Sc. (ideally Ph.D.) in statistics, mathematics, computer science, physics, aerospace engineering, or a related field.
  • •Several years of relevant professional experience in data-driven projects, preferably in an Agile environment.
  • •Proven expertise processing and analyzing aircraft sensor/maintenance or fleet data, including ILS or in-service data.
  • •Hands-on experience building health monitoring, predictive maintenance, or Integrated Logistic Support (ILS) models for the aerospace sector.
  • •Deep knowledge of statistical data analysis (e.g., probability distributions, statistical tests, anomaly detection, Bayesian inference) and Big Data development with Spark/ELT pipelines.
Experience:AerospaceBig dataMachine learningPredictive maintenanceAgile
Education:Master's
Skills:Stakeholder communicationProblem-solvingKnowledge sharingPresenting findingsData governance
Languages:EnglishGerman
Tech Stack:PythonDevOpsSQLUnixBashApache SparkSpark-SubmitELTSpotfireStreamlitDjangoMCMC

Company Brief

Airbus
Designs, manufactures, and sells commercial aircraft, helicopters, defense and space systems, and related services worldwide. Airbus is a leading aerospace and defense company delivering integrated solutions for civil and military aviation customers.
Industry: Aerospace Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Toulouse, France
Founded: 1970
WebsiteLinkedIn