- Lead the design and development of ML and decision-science solutions for high-impact operational problems, including planning, sequencing, routing, allocation, and resource optimization.
- Translate ambiguous real-world challenges into well-defined mathematical, algorithmic, or learning formulations with clear objectives, constraints, and measurable success metrics.
- Rapidly prototype and iterate using agentic coding tools and modern development workflows to accelerate experimentation, code generation, refactoring, and test creation while preserving strong engineering discipline.
- Develop, benchmark, and improve models across areas such as:
- Optimization and solver-based methods: MILP, CP-SAT, constraint programming, heuristics, metaheuristics, and search-based techniques
- Decision Intelligence and Reinforcement Learning: contextual bandits, offline RL, deep RL, Monte Carlo Tree Search, policy learning, and value-based methods
- Predictive ML: forecasting, estimation, and probabilistic models that support downstream decision systems
- Design rigorous evaluation frameworks, including simulation environments, counterfactual analysis, ablation studies, stress testing, and scenario-based performance assessment.
- Define KPIs, acceptance criteria, and experimentation standards to ensure solutions are both scientifically sound and operationally relevant.
- Partner closely with ML engineers and platform teams to productionize models, with attention to latency, throughput, reproducibility, monitoring, versioning, and safe deployment practices.
- Provide technical leadership in model selection, experimentation strategy, and research direction, while mentoring less experienced scientists and raising the quality bar across the team.
- Document methodologies, assumptions, results, and trade-offs clearly, and communicate recommendations effectively to both technical and business stakeholders.
- Strong experience applying machine learning and algorithmic methods to real-world decision-making or optimization problems.
- Demonstrated proficiency with agentic coding assistants and AI-supported development workflows to accelerate research and engineering output without compromising code quality, maintainability, or testing standards.
- Advanced Python skills and strong hands-on experience with ML frameworks such as PyTorch preferred, or TensorFlow.
- Solid grounding in algorithms, optimization, probability, statistics, and experimental design.
- Proven ability to structure messy, high-ambiguity business problems into tractable technical solutions with measurable impact.
Strong communication skills, with the ability to explain complex technical concepts, experimental findings, and trade-offs to diverse stakeholders.
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