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. 
• 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.