Flight physics ARtificial intelligence and Machine learning Data Scientist - FARMer

Job Description:

Flight Physics CoC is looking for a Machine Learning and Data Scientist Engineer to join our Flight Physics FARM team. 

The objective of the Flight Physics “FARM” team is to reinforce the technical and operational leadership in the 1G Artificial Intelligence ecosystem and be in position to support all the Flight Physics business needs. 

You will join the transnational Core Team, composed of 3-5 full-time data scientists and AI specialists, serves as the central technical execution engine and primary Doers (FARMer) tasked with moving AI concepts to working prototypes. This team translates high-level business needs into technical requirements, develops initial validated models, and drives the adoption of standardized MLOps and POD platforms to ensure technical consistency across the Flight Physics organization. Critically, they also partner with the domains and Technical Experts to define and execute the Verification & Validation (V&V) strategy essential for future certification.

Accoutabilities:

The central team of data scientists and AI specialists in 1GZT is accountable for:

• Technical Expertise: They are the deep-level experts responsible for selecting, developing, and applying advanced AI techniques, such as LLMs, CNNs, and creating surrogate models.

• Framing & Prototyping: Their primary focus is on rapid prototyping and feasibility studies for high-impact use cases (like the "Vibration Killer" lighthouse projects). They translate high-level business needs into technical requirements and develop the initial, validated AI models.

• Centralized Standards: They are responsible for driving the adoption of standardized tools and platforms like MLOps and POD for training and deployment, ensuring technical consistency across the Flight Physics organization.

• V&V Partnership: They work with the domains' Technical Experts to define and execute the Verification & Validation (V&V) strategy for the prototypes, which is critical for future certification.

Main activities

• Data Analytics: Apply your data science expertise to process, analyze, and interpret flight physics data.

• Process Improvement: Identify and drive process improvements, build compelling business cases, and test, mature, and industrialize these improvements to enhance our  capabilities.

• Community Building: Build and foster the FARM community in flight physics to promote exchange, mutual support, and co-creation.

• Networking: Connect with data science networks both within and outside Airbus to share knowledge and best practices.

• Team Support: Support and collaborate with aerodynamic engineering teams in their daily tasks, especially during peak workloads from industrial programs such as wind tunnel testing, numerical simulation, analysis, and modeling.

• Leadership: Mentor and inspire technical teams, introducing data science methods to drive innovation and efficiency in engineering organizations.

• Prototyping for Industrialization: Frame and build high-impact prototypes (e.g. Vibration Killer, OAD Surrogates). Prepare for hand-over for industrialization by building up MLOps and POD deployment workflows

• Domain Consulting: Act as a "detached" specialist within Aerodynamics, Loads, or Mass domains to identify pain points, define business cases (ROI), and translate engineering needs into technical AI requirements.

• Data Governance: Establish data ownership, improve data traceability, and ensure that "fuel for AI" is accessible, clean, and standardized across 1G.

• Supplier management: Manage external partners on technical deliverables for computing infrastructure, data engineering where needed.

Desired skills and qualifications

• Master’s degree in Aerospace Engineering, Mathematics, Data Science, Computer Science, or a related field.

• Proven track record in data science and analytics with at least 5 years of professional experience, including experience in AI and big data analytics.

• Proficiency in Python and fluency in the core data-science stack (NumPy, SciPy, Pandas, Scikit-Learn, PyTorch, Jupyter).

• Experience with SQL and basic proficiency in C/C++ and Fortran are additional assets.

• Familiarity with the design and maintenance of robust ETL pipelines (Airflow, Spark, or equivalent) and databases (on-premises or in cloud).

• Hands-on experience with major cloud analytics stacks (AWS, Azure, Google Cloud) for large-scale processing and model serving.

• Experience implementing experiment and model tracking for reproducibility and suitable for regulatory reviews (e.g., MLflow, DVC).

• Knowledge in Flight Physics in the aerospace context is mandatory

• 5+ years Experience in aircraft design, characterization, simulation is mandatory.

• Experience in the aerospace industry or a related field will be valuable. In particular, experience with engineering or physics-based simulations is a strong plus.

• Excellent interpersonal and communication skills, with the ability to work effectively in a team environment.

• Strong analytical and problem-solving skills, with a focus on data-driven decision-making.

• Ability to explain complex methods and the strategic value of data science to both engineers and senior managers.

• Integrity, confidence to speak up, and a commitment to act with integrity.

• Evident ability to learn and grow fast.

• Good analytical capabilities, assertive and stress resilient.

• Proven experience mentoring technical teams and introducing data-science methods in engineering organizations.

• Supplier management experience (Mandatory)

• Advanced proficiency in English.

• Willingness to travel as needed.

• Commitment to compliance and integrity.

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.

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.

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