Machine Learning Scientist

The Peninsular and Oriental Steam Navigation Company; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

The Peninsular and Oriental Steam Navigation Company is seeking an early-career ML/Data Science professional to design and implement machine learning solutions for planning and routing problems in a fast-paced environment.

You will prototype rapidly using agentic coding assistants, develop models across optimization, RL, and forecasting domains, and collaborate with ML engineers to ensure robust production readiness and clear documentation.

Qualifications

• 3+ years hands-on ML/data science/applied research (internships, thesis, projects count)
• Experience using agentic coding assistants to accelerate iteration without compromising code quality
• Strong Python skills and ML tooling familiarity (PyTorch preferred; TensorFlow acceptable)
• Solid foundations in algorithms, probability/statistics, and experimental design
• Ability to translate messy real-world problems into clear formulations and measurable metrics

Responsibilities

• Build ML solutions for decision-making problems: planning, sequencing, routing, allocation, resource utilization.
• Prototype quickly with agentic coding tools: generate scaffolds, refactor, write tests, iterate experiments with discipline.
• Develop and evaluate models in: Optimization/solvers (MILP/CP-SAT, heuristics), DL/Decision Intelligence (RL baselines, offline RL, bandits, MCTS), Predictive ML feeds for decision systems.
• Design robust evaluation harnesses: offline simulation, counterfactual testing, ablations, scenario analysis; define KPIs and thresholds.
• Collaborate with ML engineers to productionize: latency/throughput constraints, monitoring, reproducibility, versioning, safe rollout.
• Write clear technical docs and present findings to technical and non-technical stakeholders.

Skills

Python

PyTorch

ML tooling

probability/statistics

experimental design

Tools

OR-Tools

RLlib

Stable Baselines

SQL

Docker

Git

MLflow

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.