大模型和算法负责人

Job overview and responsibility

• Build and lead a high-performing advanced analytics capability spanning operations research, machine learning, and applied AI, with responsibility for team structure, hiring, onboarding, coaching, and technical standards.

• Define and enforce the team’s engineering and scientific delivery practices, including coding conventions, peer review, model documentation, experiment tracking, evaluation discipline, and clear definitions of done for algorithm deliverables.

• Own end-to-end algorithm delivery for applied AI and optimisation use cases, translating ambiguous business problems into well-formulated solutions with measurable outcomes and agreed success criteria.

• Take a hands-on leadership role in early engagements by shaping models, writing or reviewing code, validating outputs, and setting the technical benchmark for future delivery teams.

• Partner with business stakeholders, product teams, and engineering leads to scope use cases, confirm data readiness, assess feasibility, estimate algorithm effort, and align delivery plans with operational priorities.

• Support strategic pre-sales and solutioning activities by shaping technically credible, commercially compelling AI and optimisation solutions for complex operational environments.

• Present complex modelling approaches, technical trade-offs, delivery risks, and business value clearly to client operations, engineering, and leadership stakeholders.

• Coordinate cross-entity delivery with offshore and distributed teams by defining component boundaries, data requirements, handover points, governance expectations, and quality controls for each engagement.

Required skills and experiences

• Master’s or PhD degree in Operations Research, Computer Science, Applied Mathematics, Statistics, Machine Learning, Artificial Intelligence, or a closely related quantitative discipline.

• 10+ years of experience building machine learning, optimisation, or advanced algorithmic systems, including at least 3 years leading technical teams with hiring, coaching, and delivery accountability.

• Proven track record delivering algorithmic solutions into production within complex operational domains such as aviation, aerospace, manufacturing, logistics, mobility, or other mission-critical environments.

• Recent hands-on proficiency in Python, with production experience in optimisation solvers such as Gurobi, CPLEX, COPT, or OR-Tools, and/or modern ML frameworks such as PyTorch, scikit- learn, or equivalent technologies.

• Strong ability to translate business and operational challenges into mathematically sound formulations, measurable objectives, and practical implementation plans.

• Demonstrated leadership in technical governance, including model review, code quality, experiment tracking, production readiness, documentation, and delivery risk management.

• Excellent English communication skills, with the ability to engage confidently with senior stakeholders, client teams, engineers, data scientists, and cross-border delivery teams.

Preferred skills and experiences

• Domain expertise in airline, airport, MRO, aircraft OEM, or large-scale industrial operations, particularly in areas such as maintenance scheduling, revenue management, fleet assignment, crew or resource optimisation, production planning, or asset utilisation.

• Experience building a new technical function or specialist capability from the ground up, whether in a startup, new subsidiary, new site, or transformation environment within an established organisation.

• Experience operating in multinational, cross-border, or distributed delivery models with clear data boundaries, governance controls, stakeholder alignment, and handover responsibilities.

• Personal delivery experience across both operations research and machine learning, with the ability to bridge mathematical modelling, data-driven methods, engineering implementation, and business adoption.

• Recognised contribution to the algorithm, optimisation, or applied AI community through publications, conference participation, open-source work, industry research, competitions, or equivalent technical achievements.