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 robust 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

📌 Machine Learning Scientist (India)
🏢 DP World
📍 India