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.
Qualifications, Experience And Skills
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.
Expertise in Python, PyTorch, OR-Tools and solver stacks, RL libraries such as Ray RLlib or Stable Baselines, SQL, Docker, Git, MLflow, and cloud platforms.
