DP World is hiring for an FRESHER entry level Machine Learning Scientist role in India

Position:

• Machine Learning Scientist (Decision Intelligence, Applied AI, and Operations Research Specialist).

Company:

• DP World (Dubai Port World - Global Supply Chain, Port Operations, and Smart Logistics Infrastructure Leader).

Location:

• Embassy Golf Links Business Park, Bangalore, Karnataka, 560071, India.

Job type:

• Permanent, Full-time Professional Employment.

Job mode:

• Onsite / Office-based Collaborative Tech Environment.

Job requisition id:

• 15097

Years of experience:

• 0 to 5 Years (Open to fresh graduates, early-career researchers, internship holders, and experienced machine learning practitioners).

Company description

• DP World stands as a prominent global leader in international supply chain solutions, maritime trade infrastructure, terminal operations, and logistics management, headquartered in Dubai, United Arab Emirates.

• The organization owns and operates an extensive international footprint consisting of marine terminals, inland container depots, economic trade zones, feeder shipping services, and tech-driven logistics hubs spanning across six continents.

• As an indispensable backbone of world trade, DP World facilitates a major portion of international container shipping throughput, directly enabling global economic flow and connecting vital global supply chains across Asia, Europe, Africa, the Americas, and Oceania.

• The company is actively undergoing an enterprise-wide digital transformation designed to democratize artificial intelligence, continuous optimization, mathematical modeling, and autonomous decision-making across all operational touchpoints.

• By seamlessly fusing physical marine and logistics infrastructure with advanced software platforms, DP World aims to streamline port congestion, eliminate vessel turn-around delays, optimize container handling workflows, and reduce greenhouse gas emissions across global trade routes.

• Driven by cutting-edge technology, DP World integrates Internet of Things (IoT) monitoring systems, satellite tracking infrastructure, operational research platforms, and predictive deep learning algorithms to orchestrate end-to-end multimodal transportation systems.

• The technological center in Bangalore serves as an advanced artificial intelligence innovation engine, pioneering foundational machine learning research, operations research solver architectures, and automated decision intelligence engines.

• DP World fosters a highly inclusive, diverse, and vibrant work ecosystem composed of thousands of professionals representing over one hundred nationalities worldwide, promoting cross-border collaboration and creative technical experimentation.

• The overarching organizational mission focuses on pioneering smart, resilient, and eco-friendly supply chain solutions that enhance operational transparency, lower carbon footprints, and empower international economic trade.

• The enterprise remains steadfast in its dedication to corporate governance, environmental stewardship, equal opportunity employment, and ethical artificial intelligence deployment across all global business verticals.

Profile overview:

• The Machine Learning Scientist position at DP World is a high-impact technical role designed to research, design, build, and deploy advanced algorithmic solutions to address complex, high-dimensional operational decision-making problems.

• The successful candidate will spearhead the development of state-of-the-art decision intelligence models, combinatorial optimization frameworks, and predictive machine learning systems that power international shipping and supply chain networks.

• Core technical responsibilities center around formulating mathematical solutions for complex industrial challenges, including intermodal freight routing, vessel berth allocation, container sequence planning, crane assignment, and resource scheduling under dynamic operational constraints.

• The role heavily leverages modern agentic software development techniques and modern AI coding environments (such as Claude Code-style agent workflows) to rapidly prototype solution scaffolds, refactor legacy code bases, generate comprehensive test suites, and accelerate experimental iteration without compromising engineering quality.

• The ML Scientist will develop, benchmark, and maintain advanced mathematical optimization models utilizing Mixed Integer Linear Programming (MILP), Constraint Programming (CP-SAT), heuristic search methods, metaheuristics, dynamic programming, and constraint logic solvers.

• A primary area of technological exploration involves Deep Reinforcement Learning (Deep RL) and Decision Intelligence, encompassing policy gradient methods, off-policy actor-critic architectures, offline reinforcement learning, imitation learning, contextual multi-armed bandits, and Monte Carlo Tree Search (MCTS) planning frameworks.

• In addition to decision models, the ML Scientist will engineer robust predictive machine learning pipelines, building high-precision time-series forecasting systems, estimation engines, and spatial-temporal data analytics frameworks that feed underlying decision models.

• The candidate will design, build, and maintain continuous evaluation harnesses, including offline simulation frameworks, counterfactual policy testing frameworks, stress-testing pipelines, ablation study workflows, and clear quantitative key performance indicator (KPI) dashboards.

• The position requires close cross-functional collaboration with machine learning engineers, MLOps specialists, software architects, and domain operations experts to transition research-grade models into production environments operating under strict real-time latency, throughput, and reliability constraints.

• The candidate will be expected to adhere to high software engineering standards, writing clean, modular, maintainable, and thoroughly tested Python code within continuous integration and continuous deployment (CI/CD) framework architectures.

• Excellent technical documentation and communication capabilities are critical, as the role involves documenting technical architectures, authoring research design specifications, and communicating complex quantitative findings to executive stakeholders and non-technical business partners.

• This opportunity grants direct access to massive real-world operational datasets generated by worldwide terminal networks, offer direct influence over global shipping efficiency, and allows candidates to build production AI systems operating at massive international scale.

Qualifications:

• Educational Requirements: Possession of a Bachelor's degree, Master's degree, or Doctorate (Ph.D.) in Computer Science, Data Science, Operations Research, Industrial Engineering, Applied Mathematics, Artificial Intelligence, Statistics, or a closely related quantitative field.

• Prior Work Experience: 0 to 5 years of relevant hands-on exposure in applied machine learning, data science, mathematical optimization, or applied research (comprehensive academic research, graduate thesis projects, and detailed personal project portfolios count toward this requirement).

• Agentic Coding Proficiency: Proven experience integrating agentic software engineering assistants and modern AI-assisted coding tools (e.g., Claude Code, Cursor, or similar agentic development workflows) to accelerate code generation, automated testing, and research iteration without degrading code architecture or performance.

• Programming Expertise: Strong proficiency in Python software development, featuring a solid understanding of object-oriented design, modular code structures, data structures, algorithm complexity, and standard computer science principles.

• Machine Learning Toolkit: Deep familiarity with standard deep learning and machine learning libraries, with a primary preference for PyTorch (or TensorFlow), alongside scientific computation libraries including NumPy, Pandas, SciPy, and Scikit-Learn.

• Quantitative Foundations: Comprehensive knowledge of core mathematical concepts, including linear algebra, vector calculus, discrete mathematics, probability theory, mathematical statistics, and rigorous experimental testing methodology.

• Analytical Problem Formulation: Demonstrated ability to analyze ambiguous, real-world operational challenges and translate them into formal mathematical formulations, objective functions, numerical constraints, and clear evaluation metrics.

• Deep Reinforcement Learning (Preferred Plus): Practical exposure to or academic research in modern Deep RL methodologies, such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), Deep Q-Networks (DQN), offline RL algorithms, imitation learning, and tree-search planning frameworks.

• Environment & Simulator Development (Preferred): Practical experience building simulation environments, configuring reward function landscapes, debugging policy training instabilities, tuning hyper-parameters, and managing digital twin representations.

• Solvers & Optimization Tools (Preferred): Familiarity with Google OR-Tools, commercial or open-source MILP/CP solvers (Gurobi, CPLEX, CBC, SCIP), constraint programming frameworks, genetic algorithms, simulated annealing, and custom local search heuristics.

• MLOps & Development Infrastructure: Basic working knowledge of modern software development infrastructure, including Git version control, Docker containerization, MLflow for experiment tracking, CI/CD automated pipelines, and cloud computing platforms (AWS, Azure, or GCP).

• Domain Knowledge (Nice-to-Have): Exposure to industrial engineering, maritime logistics, global freight supply chain operations, fleet management, or inventory distribution systems.

Additional info:

• Strategic Workplace Location: Located at the state-of-the-art Embassy Golf Links Business Park in Bangalore, providing modern amenities, collaborative workspaces, and an inspiring environment for technology research and development.

• Equal Employment Opportunity Commitment: DP World maintains strict principles of Equal Employment Opportunity (EEO), hiring purely based on skills, merit, and experience regardless of age, gender, race, disability, religion, belief, or background.

• International Career Pathway: Provides extensive continuous learning frameworks, global cross-team collaboration platforms, mentorship programs, research publication avenues, and long-term career growth opportunities within a global corporation.

• Application Data Governance: Submitting an application authorizes DP World to securely process, transfer, and maintain candidate information within its global recruitment database for current and future vacancy evaluations.

• Technical Tooling Environment: Opportunity to work with modern technological tools including Python, PyTorch, Ray RLlib, Stable Baselines3, OR-Tools, SQL, Docker, MLflow, and cloud computing infrastructure.

• Mentorship & Team Culture: Access to direct mentorship from seasoned Machine Learning Scientists, Operations Research experts, and Principal Engineers dedicated to supporting personal skill advancement and team innovation.

• Research & Experimentation Culture: Promotes an environment where technical curiosity, experimentation with emerging artificial intelligence techniques, hackathon participation, and creative problem-solving are actively supported.

• Comprehensive Total Rewards: Competitive salary structures, performance-linked incentives, comprehensive medical health benefits, life insurance coverage, and professional learning allowances.

• Meaningful Industry Impact: Direct involvement in building mathematical models that optimize millions of global shipping containers annually, reducing operational friction, fuel burn, and environmental impact across global transportation networks.

Please click here to apply.