Machine Learning Scientist

KEY ACCOUNTABILITIES ● 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 (Py. Torch preferred; Tensor. Flow 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, Py. Torch, OR-Tools / solver stacks, RL libraries (Ray RLlib / Stable Baselines), SQL, Docker, Git, MLflow; cloud platforms a plus.

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