Machine Learning Scientist

DP World; Bengaluru; On-site; INR 800,000 - 1,200,000; Full time

Job summary

A global logistics firm in Bengaluru is looking for a Machine Learning Engineer to build solutions for decision-making problems. The suitable candidate will have 0-5 years of experience in applied machine learning or data science, strong Python skills, and familiarity with ML frameworks like PyTorch. You will work with cross-functional teams to prototype and productionize ML models, ensuring high code quality and clear documentation. Join us to impact logistics through innovative machine learning applications.

Qualifications

• 0-5 years experience in applied ML/data science.
• Strong Python skills and familiarity with ML tooling.
• Solid foundations in algorithms and statistics.

Responsibilities

• Build ML solutions for decision-making problems.
• Prototype fast using agentic coding tools.
• Collaborate with ML engineers for productionization.

Skills

Applied Machine Learning

Data Science

Python

ML workflow tools

Tools

PyTorch

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 such as optimization & solvers (MILP/CP‑SAT, heuristics, constraint programming, search methods), Deep RL/Decision Intelligence (RL baselines, offline RL, bandits, MCTS‑style planning, policy/value learning), and predictive ML (forecasting and estimation models that feed decision systems).
• Design robust evaluation harnesses: offline simulation, counterfactual testing, ablations, 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) to accelerate iteration—without sacrificing code quality.
• Strong Python skills and comfort with ML tooling (PyTorch preferred; TensorFlow okay).
• 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.