Senior Business Analyst – Optimisation

Responsibilities

• Design, write, and maintain mathematical optimisation models and decision-support tools for: 

• Crew: pairing construction, roster generation, reserve planning, training integration, accommodation/transport cost optimisation 

• Fleet/Aircraft: tail assignment/aircraft routing, maintenance-integration, aircraft swaps 

• Network/Schedule: schedule feasibility and robustness, turn times, curfew/airport constraints, block time standards 

• Disruption Management: recovery optimisation for aircraft and crew, with passenger reaccommodation interfaces 

• Resource Planning: manpower forecasting/base sizing, ground resource/gate assignment (as applicable) 

• Lead end-to-end development and deployment of optimisers, including modelling, algorithm design, parameter tuning, validation, and operationalisation. Define functional/non-functional requirements, manage backlogs, and run UAT with end users. 

• Collaborate with Crew Planning, Rostering, Flight Operations, OCC, Network Planning, Maintenance/Engineering, Ground Operations, Finance, Revenue Management, and IT teams to ensure optimisation outputs align with business rules, regulatory requirements, airport/airspace constraints, maintenance plans, labour agreements, and safety standards. 

• Analyse crew and aircraft utilisation, robustness, and cost drivers (e.g., block hours per tail/FTE, reserve coverage, deadhead/hotel/transport, OTP, completion factor, reactionary delay) to prioritise and refine optimisation opportunities. 

• Work with software vendors to enhance or integrate optimisation capabilities into existing systems; evaluate build vs buy options, support RFPs, and manage vendor deliverables. 

• Continuously refine models based on operational feedback, data trends, and crew/operations satisfaction metrics; ensure model explainability and auditable constraints. 

• Establish governance and versioning for business rules, parameters, and constraint changes; maintain data quality standards and SLAs with data providers. 

• Define KPIs and benefits frameworks; run experiments (backtests, A/B tests, shadow runs) to validate value vs baseline and ensure benefits realisation with Finance. 

• Provide technical leadership and mentorship within the optimisation team and across related projects; contribute to strategic roadmaps and innovation in digital operations and crew enablement. 

• Present findings, scenarios, and optimisation recommendations to senior leadership and cross-functional stakeholders; drive adoption through training, playbooks, and change management. 

Qualifications

• PhD or MSc in Mathematics, Operations Research, Computer Science, or related field with specialisation in Optimisation. 

• Minimum 8+ years of hands-on experience developing and deploying optimisation models, preferably in aviation, transport, or logistics, with demonstrable outcomes across crew, fleet/aircraft routing, schedule design, or disruption recovery. 

• Strong understanding of crew pairing, rostering, and fatigue management regulations, plus working knowledge of aircraft/maintenance integration, tail assignment, and day-of-operations decision-making. 

• Proficient in linear/integer programming, heuristic/metaheuristic methods, and solver technologies (e.g., CPLEX, Gurobi). 

• Experience with modern programming languages such as Python, C++, or Java. 

• Familiarity with airline scheduling software suites and crew management systems (AIMS, Jeppesen, etc.) is highly desirable; exposure to OCC tools and tail assignment/recovery systems is a plus. 

• Demonstrated ability to lead cross-functional projects and influence without authority in operational and unionised environments, Agile delivery experience. 

• Excellent communication skills – capable of explaining complex models, trade-offs, and solver outcomes to both technical and non-technical audiences; strong stakeholder and change management skills. 

Preferred Attributes

• Prior experience in airline operations (Crew Planning, OCC, Network, or Maintenance/Engineering). 

• Exposure to machine learning for demand/absence forecasting, delay propagation, disruption recovery, or maintenance reliability. 

• Experience measuring value realisation (cost savings, utilisation gains, robustness improvements) via experiments and operational pilots. 

• Vendor and RFP experience for optimisation platforms and integrations; cloud/data platform familiarity.