{"id":1171165,"url":"https://alion.io/job/airbus-data-scientistdigitalization-engineer-system-standard-parts-mfd","title":"Data Scientist/Digitalization Engineer – System Standard Parts (m/f/d)","company":{"id":3100,"name":"Airbus","domain":"airbus.com","url":"https://alion.io/company/airbus","size_band":"5000+","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":99,"open_postings":112,"ghost_share":0,"stale_share":0.009,"repost_share":0.036,"time_to_fill_p50_days":25,"computed_at":"2026-09-24T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":54000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"GCP","optional":true},{"name":"JavaScript","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"SciPy","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-09-24T07:34:57Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-24T13:56:21Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Description:\nAt Airbus, our goal is to \"pioneer sustainable aviation for a safe and united world\". To achieve this, the System Standard Partsteam is accelerating the digital transformation of our engineering cycle.\nWe 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 V&V efficiency to ensure 'right first time' outcomes.\nWhat we offer:\nA diverse and international team cross collaborating and supported by an agile and open-minded management team.\n\nWork at the intersection of engineering and advanced analytics in an aerospace environment.\n\nA dynamic and fast-paced business environment where your experience, expertise and knowledge directly impacts day-to-day business operations.\n\nThe opportunity to grow continuously in your field of expertise and develop yourself supported by professional training and a network of enthusiastic and knowledgeable colleague\n\nResponsibilities\nThe candidate will drive the application of data science across the domain, focusing heavily on leveraging unstructured data (technical reports, specifications, and documentation).\nGenerative 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.\n\nModel 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).\n\nInsight Generation & Visualization: Create rapid, impactful visualizations and demos to effectively communicate complex data findings and model outputs to engineering stakeholders .\n\nPrompt Engineering & Workflow: Skillfully craft and refine prompts to maximize the effectiveness and quality of LLM outputs for technical use cases\n\nUpskilling 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.\n\nKnowledge Management - Support team to create/manage Knowledge database and align with Airbus KM goals.\n\nStakeholder Engagement: Work closely with cross-functional teams (e.g., Design, Certification, Manufacturing) and business stakeholders to understand project requirements and provide analytical support.\n\nWho We Are Looking For\nPreferably, possess a Bachelor's or Master's degree in Computer Science\n\n5-8 years of professional experience in digitalization or engineering data science.\n\nExperience in Design of Experiments (DoE) and statistical software (e.g., JMP, Minitab, or Python-based libraries).\n\nProven track record in developing and deploying Machine Learning models in a physical engineering or manufacturing context.\n\nProficiency in Python (SciPy, Scikit-learn, TensorFlow/PyTorch) and JavaScript/Apps Script\n\nCapability in building enterprise-level solutions with Google AppSheet and managing Google Cloud/Workspace integrations.\n\nYou demonstrate networking skills to collaborate with universities, research centers and external partners.\n\nStrong communication skills .\n\nCandidates who demonstrate prior exposure in Electrical / Mechanical / Aerospace Systems Engineering will have an added advantage.\n\nIf your profile matches our requirements and you are interested in joining our dynamic and multicultural team of professionals, you can complete your application online .\nThis 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.\nCompany:\nAirbus India Private LimitedEmployment Type:\nPermanent-------\nExperience Level:\nProfessionalJob Family:\nDigital 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.\nAirbus 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.\nAirbus 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 .\nAt Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.","description_format":"text","description_chars":5238,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Flexible schedule"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Earth Observation","Combat Aircraft","Defense Manufacturing","Avionics & Flight Control"],"lifecycle":[{"event":"open","at":"2026-09-24T07:34:57Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":25,"reasons":["conf:1","win:early","comp:brand"],"computed_at":"2026-09-24T15:29:20Z"},"pay":null,"html_url":"https://alion.io/job/airbus-data-scientistdigitalization-engineer-system-standard-parts-mfd","json_url":"https://alion.io/job/airbus-data-scientistdigitalization-engineer-system-standard-parts-mfd.json","meta":{"generated_at":"2026-09-24T15:29:20Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}