Machine Learning Scientist

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.

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