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 (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.

#LI-MP1