Job Description: The Big Data Team (working mode: Agile Scrum) is searching for an experienced data scientist. The project you will be working on represents Airbus Defence and Space's analytics capabilities for our largest customers and all our large aircraft. It delivers improved performance and customer experience and enables a rapid understanding of the potential of digitalisation for our customers. You will be at the forefront of further developing our Data Analytics solutions. The main task will be to find new ways to address challenges with data and digital technologies and support the creation of new services. We have well over 100 data products and operate more than 50 use cases in production. In addition, we already have a data pool of several terabytes. Tasks: Product discovery and maturation: Ask the right questions to support the discovery process Identify the data-analytics problems that offer the greatest opportunities to the organization Perform exploratory data analysis using visualization and sense You are or will be familiar with our business and actively follow market trends You present your findings to stakeholders using visualisation and other means Data structuring: Collating data and "data wrangling", i.e. transforming, cleansing and linking it with other data. Providing data sets for machine data models; determining relationships between data source attributes Apply data mining techniques to carry out statistical analysis. Create and validate models, also but not only using ML Data products: Exploratory analyses of data, extraction of specific content, patching, and statistical analysis Develop and implement programme prototypes and production code, mainly in Python Supporting the execution of proof-of-concept and ad-hoc requests within the community Development and handling of MCMC simulations, as well as output analysis. Pioneering role: Driving new working methods and new data governance models. Presenting results to diverse audiences; from engineers to managers Sharing and exchanging knowledge with other teams and working closely with the different support teams and the design office. Qualifications required: M.Sc. (ideally Ph.D.) in statistics, mathematics, physics, computer science, or a related field Several years of relevant professional experience Deep knowledge and expertise in data analysis and statistical interpretations of data (e.g. probability distributions, statistical tests, Bayesian inference) Expertise in pre-processing, cleansing, and feature engineering Expertise in Machine Learning Good working knowledge of Python (in particular: pySpark, pandas, matplotlib, etc.) Secure handling of Unix systems Ideally: Expertise in DevOps and/or coding in a production environment, as well as basic knowledge of SQL technologies and databases Optional: Experience with visualisation tools (e.g. Spotfire, ) Fluent in English and ideally also in German This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth. Company: Airbus Defence and Space GmbH Employment Type: Permanent Experience Level: Professional Job Family: Configuration Management By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus. Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief. Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to emsom@airbus.com. At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.