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About the role
Machine Learning Scientist
Bangalore, Karnataka, India
Trending
Job Description
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 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 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
DP World is committed to the principles of Equal Employment Opportunity (EEO). We strongly believe that employing a diverse workforce is central to our success and we make recruiting decisions based on your experience and skills. We welcome applications from all members of society irrespective of age, gender, disability, race, religion or belief.
By submitting your resume and application information, you authorize DP World to transmit and store your information in the world-wide recruitment database, and to circulate that information as necessary for the purpose of evaluating your qualification for this or other job vacancies.
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Job Info
- Job Identification15097
- Posting Date03/10/2026, 07:32 AM
- LocationsEmbassy Golf Links Business Park, Bangalore, 560071, IN
- Degree LevelBachelor's Degree
- Job ScheduleFull time
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