KEY ACCOUNTABILITIES
Build ML solutions for decision-making problems: planning, sequencing, routing,

allocation, and resource utilization.
Prototype rapid 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 explicit 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 solid 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 Bengaluru (India)
🏢 DP World
📍 India