技能/経験

■Key Qualifications and Experience
-Must-Have Requirements-
・ Educational Background
Advanced degree (or equivalent practical experience) in Operations Research, Applied Mathematics, Energy Systems, Electrical Engineering, or related technical fields.
・ Optimization Expertise
Solid foundation in mathematical optimization, especially linear programming (LP) and mixedinteger programming (MIP). Demonstrated ability to formulate, implement, and refine constrained optimization problems using industry-standard solvers such as Gurobi (or equivalent).
・ Programming Proficiency
Strong Python programming skills, particularly with object-oriented development for scientific computing. Able to write maintainable and modular code for optimization routines and model configuration.
・ Analytical and Diagnostic Skills
Ability to interpret solver output, identify bottlenecks or infeasibilities, and troubleshoot convergence issues. Rigorous attention to detail and a structured approach to model validation and performance tuning.

-Want-Have Requirements-
・ Stochastic Optimization Knowledge
Familiarity with uncertainty modelling techniques such as stochastic programming or robust optimization, especially as applied to long-term planning or dispatch under uncertain conditions.
・ Power Systems and Market Knowledge
Broader understanding of power market operations, merit-order pricing, balancing markets, or regulatory constraints in electricity systems. Knowledge of how these are reflected in real-world dispatch models is a plus.
・ Applied Experience in Energy Systems
Hands-on experience applying optimization techniques in the context of electricity systems,power scheduling, or energy forecasting. Strong understanding of how generation constraints,forecast uncertainty, or operational limitations translate into mathematical formulations.
・ Tooling and Infrastructure Experience
Exposure to Linux-based environments, version control (e.g., Git), and data interfaces such as SQL or NoSQL databases. Experience working in cloud-based modelling environments is advantageous.
・ Solver Flexibility
Familiarity with other optimization solvers (e.g., CPLEX, HiGHS, CBC/CLP), including trade-offs in performance and licensing.
・ Cross-Functional Collaboration
Experience working in collaborative teams that include analysts, model developers, and scheduling operators, especially in large-scale modelling or infrastructure planning contexts.
・ Fluency in both Japanese and English
For Japanese nationals, the ability to communicate effectively in English in a business environment, both verbally and in writing, is expected. For non-Japanese nationals, Japanese language skills are good to have; experience working with Japanese materials or data sources would be an advantage.

■Person Specification
・ Curious and proactive, with strong analytical thinking and problem-solving skills.
・ Effective communicator, able to collaborate across functions and drive analytical outcomes.
・ Strong interest in energy markets and trading, with good commercial awareness.
・ Highly responsible, result-oriented, with good time management and planning ability.
・ Able to thrive in a fast-changing market environment with a strong sense of initiative.