About this role
KEY ACCOUNTABILITIES
• 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.
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