Key Accountabilities JOB DESCRIPTION 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. What We’re Looking For (Required) 0–5 years experience in applied ML / data science / applied research (internships, thesis work, and strong project portfolios count). Demonstrated experience using agentic coding assistants in real development (e.g., Claude Code, similar agentic coding environments) to accelerate iteration—without sacrificing code quality. Strong Python skills and comfort with ML tooling (PyTorch preferred; TensorFlow ok).Solid foundations in algorithms, probability/statistics, and experimental design.Ability to translate messy real-world problems into clear formulations and measurable success metrics. Strong Plus / Preferred Prior work in Deep RL (a strong differentiator), such as: ○ PPO/SAC/DQN style methods, offline RL, imitation learning, MCTS/planning hybrids ○ Building environments/simulators, reward design, stability/debugging, evaluation Experience with simulation-based evaluation or digital twins (even lightweight simulators). Familiarity with MLOps basics: MLflow, Docker, CI/CD, model monitoring.Domain exposure to logistics/supply chain/industrial operations (nice-to-have, not required). Tools & Tech (Indicative) Python, PyTorch, OR-Tools / solver stacks, RL libraries (Ray RLlib / Stable Baselines), SQL, Docker, Git, MLflow; cloud platforms a plus.