1. Role Scope & Accountability
Accountable for the end-to-end lifecycle of optimization algorithms that improve asset performance, availability, cost efficiency, and risk. This includes problem formulation, model development, testing, deployment, monitoring, and continuous improvement in production environments.
2. Required Education & Background
- Master’s or PhD in Operations Research, Applied Mathematics, Industrial Engineering, Systems Engineering, Computer Science, or Data Science
- Strong grounding in:
- Optimization theory (linear, nonlinear, mixed-integer, stochastic)
- Control theory or decision sciences (preferred for dynamic assets)
- Equivalent industry experience may substitute for formal education
3. Core Technical Competencies
Optimization & Algorithms
- Proven experience designing and implementing:
- Mathematical optimization models (LP, MILP, MINLP)
- Heuristic and metaheuristic methods (genetic algorithms, simulated annealing, tabu search)
- Multi-objective optimization and constraint handling
- Ability to translate business and operational constraints into formal optimization problems
Data & Modeling
- Strong statistical modeling skills
- Experience working with:
- Time-series data from physical assets
- Uncertain, incomplete, or noisy operational data
- Model validation, sensitivity analysis, and robustness testing
Software Engineering & Deployment
- Proficiency in Python; experience with GAMS, Java, or Julia is a plus
- Hands-on experience with:
- Optimization solvers (e.g., Gurobi, CPLEX, CBC, SCIP)
- ML frameworks if hybrid approaches are used
- Production deployment experience:
- API-based model serving
- CI/CD pipelines
- Model versioning and rollback strategies
- Familiarity with cloud environments (AWS, Azure, or GCP)
4. Asset & Domain Knowledge
- Experience in at least one asset-intensive industry, such as:
- Energy, utilities, oil & gas
- Manufacturing or process industries
- Transportation, logistics, or infrastructure
- Understanding of:
- Asset lifecycle management
- Maintenance optimization (preventive, predictive, condition-based)
- Reliability, availability, maintainability (RAM) concepts
5. Testing, Validation & Governance
- Strong experience with:
- Offline backtesting and scenario analysis
- A/B testing or shadow-mode deployment
- Performance monitoring and KPI definition
- Ability to define acceptance criteria for algorithmic performance
- Understanding of algorithm governance, auditability, and explainability
6. Leadership & Collaboration
- Technical leadership:
- Mentoring engineers and scientists
- Setting coding, modeling, and documentation standards
- Cross-functional collaboration with:
- Asset management
- Operations and maintenance
- IT and platform engineering
- Ability to challenge requirements constructively and manage trade-offs
7. Business & Strategic Skills
- Strong problem framing and prioritization skills
- Ability to:
- Quantify value creation (cost reduction, uptime improvement, risk mitigation)
- Communicate algorithmic decisions to non-technical stakeholders
- Experience aligning algorithm roadmaps with business objectives
8. Preferred Experience
- 4+ years of relevant industry experience
- Prior ownership of production optimization systems
- Experience with digital twins or decision-support platforms
- Exposure to regulatory or safety-critical environments
9. Key Success Indicators
- Measurable improvement in asset KPIs driven by deployed algorithms
- High adoption rate by operations teams
- Stable, explainable, and maintainable optimization solutions in production
