Explore Exciting Fresher & Experienced Roles at DP World

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