This is a senior data scientist position for our mathematical optimization/operations research area, expanding on his/her prior repertoire of skills in data science and machine learning to include exact/approximate methods for linear and nonlinear optimization problems. 

Key responsibilities

• Collaborate with cross-functional teams (business stakeholders, domain experts, engineers) to identify and frame challenging problems in mathematical optimization and operations research.
• Design, develop, and deploy optimization solutions that have direct impact on aircraft assignment, crew scheduling and other related operational areas, including predictive models to required forecast demand, disruptions, or operational KPIs. Validate models and solutions through rigorous testing, scenario analysis, and sensitivity studies; communicate results and trade-offs clearly to technical and non-technical audiences.
• Work closely with application development teams to help them operationalize and integrate optimization capabilities on their software systems, advise on data requirements, API design, performance considerations, and monitoring.
• Monitor and maintain production optimization services, troubleshoot issues, and iterate on models and algorithms as business needs evolve.
• Stay current on advances in operations research, optimization solvers (e.g., Gurobi), algorithmic improvements, and relevant data science techniques; evaluate new tools or libraries and recommend adoption where beneficial.

Requirements

• BS in Computer Science, Mathematics, Physics or a related discipline is required. PhD and MS degrees related to mathematical optimization are highly desired.
• Advanced programming skills in Python. Conversant with data structure, algorithm design/analysis, and object-oriented programming paradigm.
• At least 2 years of relevant technical experience in two or more of the following areas:
  • Intermediate or advanced skills in operations research, such as linear/integer/mixed-integer programming. Some familiarity with simulation, queuing theory, network and decision analyses is a plus.
  • Good understanding of some subareas of optimization algorithms, such as those in gradient-based methods or metaheuristic methods.
  • Familiarity of common Python optimization libraries, such as NumPy, SciPy, CVXPY, PuLP, and Pyomo; modern optimization platforms such as Gurobi, CPLEX or Google OR-Tools.
  • ntermediate-level hands-on skills in predictive modelling, shallow machine learning and information retrieval.Prior experience or knowledge in aviation/airlines operation is a plus.
  • Some hands-on experience with AWS, GCP or similar public cloud environment.
• Excellent interpersonal & communication skills for working with both technical staff and non-technical business users.
• Experience with Agile/Scrum/Kanban methodologies is a plus.

We thank all candidates for your interest in Singapore Airlines, and regret that only shortlisted candidates will be notified.