Senior Data Scientist (Aviation & Supply Chain AI)

Role overview

Armac is developing software that modernizes aircraft inventory planning and helps aviation maintenance move away from manual, reactive work and disconnected data. The team is building an intelligent, autonomous supply chain for aviation MRO, and this senior data science role sits at the center of that effort.

You will help shape how operational, market, and institutional knowledge is transformed into automated, proactive decisions. The position combines classical analytics, machine learning, and agentic AI to support the most important supply chain decisions that keep aircraft in service and reduce operating cost.

Working closely with the founders and subject-matter experts, you will contribute to the evolution of the RIOsys platform into an industry-standard product used by global airlines to move from manual processes to resilient, event-driven operations. This is a hands-on role for an experienced data scientist who enjoys solving difficult, high-stakes problems in a complex domain.

What you will do

• Build applied data science and machine learning models for forecasting, planning, optimisation, risk scoring, anomaly detection, and decision support.
• Study operational, transactional, historical, and planning datasets to uncover trends, patterns, gaps, and actionable signals.
• Work with aerospace specialists to understand inventory, demand, supply chain, repair, forecasting, and planning workflows.
• Improve the accuracy, reliability, and explainability of model-based recommendations.
• Decide where machine learning adds value and where statistical techniques, rule-based logic, or deterministic approaches are the better fit.
• Enhance forecasting methods for intermittent, slow-moving, variable, and high-value demand.
• Assess forecast accuracy, bias, uncertainty, and real business impact.
• Create scoring and prioritisation models that help users focus on the most critical risks and planning exceptions.
• Design recommendations that are transparent, explainable, and practical for operational users.
• Contribute to AI-powered product capabilities using large language models, retrieval-augmented generation, and workflow automation.
• Define how AI responses should be anchored to trusted data, calculations, documentation, and system outputs.
• Help evaluate AI-generated responses, explanations, and recommendations.
• Partner with software developers to turn analytical work and prototypes into production-ready product functionality.
• Collaborate with data engineering to define modelling, analytics, and AI data needs.
• Identify data quality issues, missing elements, inconsistencies, and other limitations.
• Prepare model-ready datasets, features, and evaluation sets.
• Work with Java full-stack developers on model integration, APIs, data contracts, and workflow design.
• Communicate insights clearly to both technical and non-technical audiences.

Technology environment

The role will involve working across a modern but non-exhaustive stack that includes SQL databases, text-based snapshots, operational business data, historical demand and forecast data, inventory, purchasing, repair, and planning data, Python, pandas, NumPy, scikit-learn, statsmodels, Jupyter notebooks or equivalent tools, Git, Java-based SaaS platforms, cloud-hosted production environments, data pipelines managed by the data engineering team, and growing use of LLMs, RAG, and AI-assisted workflow automation.

Required experience

• At least 5 years of experience in applied data science, machine learning, advanced analytics, or a closely related discipline.
• Strong working knowledge of Python and SQL.
• Demonstrated ability to design and deliver end-to-end AI/ML systems into production.
• Practical experience taking LLM and RAG solutions beyond proof of concept into production environments where reliability and explainability matter.
• Solid understanding of data preparation, feature engineering, model validation, and performance evaluation.
• Comfort working with messy, incomplete, or complex operational datasets.
• Ability to describe model outputs, assumptions, constraints, and uncertainty in a clear way.
• Experience in forecasting, classification, ranking, recommendations, anomaly detection, or decision-support applications.
• Capability to work independently without close data science oversight.
• Comfort collaborating with developers, data engineers, product teams, and domain specialists.
• Strong communication skills and a practical mindset focused on outcomes.

Preferred background

• Experience in aviation, aerospace, MRO, inventory optimisation, supply chain, logistics, manufacturing, or a similarly complex operational setting.
• Exposure to intermittent demand forecasting or slow-moving inventory.
• Experience with MLflow or comparable experiment/model tracking tools.
• Experience building solutions for Java-based enterprise software environments.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Awareness of SOC 2, ISO 27001, or similar security and compliance standards.

Personal qualities

• Shows strong ownership and accountability for deliverables.
• Works in a structured, analytical, and organised way.
• Is practical and solution-oriented rather than overly academic.
• Can operate independently while still collaborating closely with others.
• Communicates effectively with both technical teams and business stakeholders.
• Enjoys working with experts to understand intricate business problems.
• Comfortably challenges assumptions when needed.
• Values explainability and trust in AI and data science.
• Is motivated by ambiguous, real-world problem solving.

Additional information

This role is based in Ireland and is full-time, onsite. No salary, stipend, benefits, notice period, start date, or vacancy count was provided in the source information.