Machine Learning Scientist

● Build ML solutions for decision-making problems: planning, sequencing, routing, 
allocation, and resource utilization. 
● Prototype fast using agentic coding tools (e.g., Claude Code-style workflows): 
generate scaffolds, refactor, write tests, iterate on experiments—while maintaining 
strong engineering discipline. 
● Develop and evaluate models in areas like: 
    ○ Optimization & solvers: MILP/CP-SAT, heuristics/metaheuristics, constraint 
programming, search methods 
    ○ Deep RL / Decision Intelligence: RL baselines, offline RL, bandits, 
MCTS-style planning, policy/value learning 
    ○ Predictive ML: forecasting and estimation models that feed decision systems 
● Design robust evaluation harnesses: offline simulation, counterfactual testing, 
ablations, and scenario analysis; define KPIs and acceptance thresholds. 
● Collaborate with ML engineers to support productionization: latency/throughput 
constraints, monitoring, reproducibility, model versioning, and safe rollout. 
● Write clear technical documentation and communicate findings to both technical and 
non-technical stakeholders.