Senior AI/ML Scientist

• Design, develop, and implement machine learning models, conduct in-depth data analysis, and support decision-making with data-driven insights
• Develop predictive models and validate their effectiveness
• Support the design of experiments to validate and compare multiple machine learning approaches
• Research and implement cutting-edge techniques (e.g., transformers, GANs, reinforcement learning) and integrate models into production systems, ensuring scalability and reliability
• Apply creative problem-solving techniques to design innovative models, develop algorithms, or optimize workflows for data-driven tasks
• Independently apply data-driven solutions to ambiguous problems, leveraging tools like Natural Language Processing, deep learning frameworks, machine learning, optimization methods and computer vision frameworks
• Understand technical tools and frameworks used by the team, including programming languages, libraries, and platforms and actively support debugging or refining code in projects
• Write and integrate automated tests alongside their models or code to ensure reproducibility, scalability, and alignment with established quality standards
• Contribute to the design and documentation of AI/ML solutions, clearly detailing methodologies, assumptions, and findings for future reference and cross-team collaboration
• Collaborate across teams to develop and implement high-quality, scalable AI/ML solutions that align with business goals, address user needs, and improve performance

• Mastered Data Analysis and Data Science concepts and can demonstrate this skill in complex scenarios
• AI & Machine Learning, Programming and Statistical Analysis Skills beyond the fundamentals and can demonstrate the skills in most situations without guidance.

• To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance:

• Data Validation and Testing
• Model Deployment
• Machine Learning Pipelines
• Deep Learning
• Natural Language Processing (NPL)
• Optimization & Scientific Computing
• Decision Modelling and Risk Analysis.
• To understand fundamentals and can demonstrate this skill in common scenarios with guidance:

• Technical Documentation.

• Bachelor’s degree in B.E./B.Tech, preferably in Computer Science, Data Science, Mathematics, Statistics, or related fields.

• Strong practical understanding of:

• Machine Learning algorithms (classification, regression, clustering, time-series)
• Statistical inference and probabilistic modeling
• Data wrangling, feature engineering, and preprocessing at scale

• Proficiency in collaborative development tools:

• IDEs (e.g., VS Code, Jupyter), Git/GitHub, CI/CD workflows, unit and integration testing
• Excellent coding and debugging skills in Python (preferred), with knowledge of SQL for large-scale data operations

• Experience working with:

• Versioned data pipelines, model reproducibility, and automated model testing
• Ability to work in agile product teams, handle ambiguity, and communicate effectively with both technical and business stakeholders
• Passion for continuous learning and applying AI/ML in impactful ways

• 8+ years of experience in AI/ML or Data Science roles, working on applied machine learning problems in production settings
• 7+ years of hands-on experience with:

• Apache Spark, distributed computing, and large-scale data processing
• Deep learning using TensorFlow or PyTorch
• Model serving via REST APIs, batch/streaming pipelines, or ML platforms
• Hands-on experience with:

• Cloud-native development (Azure preferred; AWS or GCP also acceptable)
• Databricks, Azure ML, or SageMaker platforms
• Experience with Docker, Kubernetes, and orchestration of ML systems in production
• Familiarity with A/B testing, causal inference, business impact modeling
• Exposure to visualization and monitoring tools: Power BI, Superset, Grafana
• Prior work in logistics, supply chain, operations research, or industrial AI use cases is a strong plus

Maersk