DP World is hiring an Machine Learning Scientist for the Bangalore location. Please read the complete information carefully and apply if you are eligible.

Eligibility Criteria :

• Qualification: Bachelor’s degree or equivalent 

• Experience:  0 - 5 Years

• Skills: Python, PyTorch, OR-Tools / solver stacks, RL libraries (Ray RLlib / Stable Baselines), SQL, Docker, Git, MLflow; cloud platforms a plus. 

Responsibilities:

• 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.

Required Skills:

•  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.

Preferred skils:

• 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).

Stipend:  ₹15LPA – 30LPA(Glassdoor)

Apply Now