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 transparent 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.

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