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.

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