DNV is the world’s largest ship classification society, dedicated to safeguarding life, property, and the environment. We are currently establishing a new AI team in Pune to enhance our business processes and services through advanced AI and machine learning technologies. You will join a leading AI competency and development team, working in collaboration with colleagues in Pune and our headquarters in Oslo. Our mission is to leverage AI to improve global maritime work processes, support the digitalization of production systems, and create innovative digital services.
Responsibilities
Solve real-world business cases by developing and deploying AI and Machine Learning solutions.
Engage in all project phases, from stakeholder dialogue and problem definition to data analysis and technical architecture planning.
Collaborate with cross-functional teams and domain experts to experiment, program, and release solutions into production using DevOps cycles.
Actively explore new opportunities for applied AI and stay updated on industry trends to identify potential new business opportunities for DNV.
Requirements
Proven experience as a Data Scientist or in a similar AI/ML development role.
Strong programming skills and experience with AI/ML model development.
Ability to work effectively within a multi-national organization and collaborate with diverse teams.
Strong analytical skills and a proactive approach to identifying business opportunities through technology.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
End-to-End AI Solution Delivery
The role requires managing the full lifecycle of AI and machine learning projects, from initial stakeholder dialogue and problem definition to technical architecture, programming, and final deployment into production environments.
Cross-Functional and Global Collaboration
Success depends on working effectively within a multi-national organization, specifically collaborating with domain experts and colleagues across different geographic locations, such as the Pune and Oslo offices.
Proactive Business Value Identification
Beyond technical execution, the role demands a proactive mindset to explore industry trends and identify how applied AI can create new business opportunities and improve maritime work processes.
DevOps-Driven Productionization
The candidate must be capable of releasing AI models into production using established DevOps cycles, ensuring that experimental code is robust, scalable, and integrated into DNV's digital services.
Likely challenges to think through
Bridging Technical and Domain Expertise
Translating complex maritime business problems into actionable AI models requires deep collaboration with non-technical domain experts. Prepare by demonstrating experience in requirements gathering and explaining technical trade-offs to stakeholders without a data science background.
Managing Distributed Team Dynamics
Coordinating development efforts between Pune and Oslo may introduce communication or workflow synchronization hurdles. Prepare by highlighting your experience with remote collaboration tools and your ability to maintain alignment across different time zones and cultural contexts.
Balancing Innovation with Operational Stability
The dual requirement to explore new AI opportunities while maintaining production-grade DevOps cycles creates a tension between experimentation and reliability. Prepare by discussing your approach to managing technical debt and ensuring that rapid prototyping does not compromise system safety or performance.
03
Practice your answers
Technical practice
AI/ML Lifecycle Management
What to coverDescribe your experience managing the full lifecycle of an AI project, from initial problem definition to deployment. Focus on how you handle data analysis, model selection, and the transition from experimentation to production environments.
DevOps for Machine Learning
What to coverExplain your approach to integrating machine learning models into production using DevOps cycles. Discuss how you handle version control for data and models, automated testing, and continuous monitoring of model performance after deployment.
Technical Architecture Planning
What to coverHow do you approach designing the technical architecture for a new AI solution? Address how you ensure scalability, maintainability, and compatibility with existing production systems.
Data Analysis and Feature Engineering
What to coverDescribe a scenario where you had to perform deep data analysis to solve a business problem. Detail your process for identifying relevant features and how you validated that your data was sufficient for the intended model.
Model Selection and Trade-offs
What to coverWhen choosing between different machine learning algorithms for a business case, what criteria do you prioritize? Discuss the trade-offs between model complexity, interpretability, and performance in a production setting.
Staying Current with AI Trends
What to coverThe role requires identifying new business opportunities through applied AI. How do you stay updated with industry trends, and how do you evaluate whether a new technology is suitable for a specific business application?
Behavioural practice
Stakeholder Collaboration
What to coverDescribe a time you had to translate complex technical requirements to non-technical stakeholders. How did you ensure alignment on the problem definition and project goals?
Cross-functional Teamwork
What to coverProvide an example of working with domain experts or cross-functional teams to deliver a project. How did you manage differing perspectives to reach a successful outcome?
Proactive Problem Identification
What to coverGive an example of a time you identified a business opportunity or process improvement through technology that was not initially requested. What steps did you take to validate and propose this idea?
Working in a Multi-national Environment
What to coverShare an experience working within a global or multi-national organization. How did you handle communication challenges or time zone differences to ensure effective collaboration?
02
Sharpen your knowledge
Topics to prepare
End-to-End AI Project Lifecycle
The role requires engagement from stakeholder dialogue and problem definition to production deployment. Review the stages of the CRISP-DM framework or similar methodologies to articulate how you bridge the gap between business requirements and technical implementation.
Applied AI in Industrial/Maritime Contexts
DNV focuses on maritime work processes and production systems. Research how machine learning is applied to predictive maintenance, sensor data analysis, or digital twins in industrial settings to prepare for discussions on identifying new business opportunities.
Skills to revise
DevOps for Machine Learning (MLOps)
The job explicitly mentions releasing solutions into production using DevOps cycles. Practice containerization (e.g., Docker), CI/CD pipeline construction for model deployment, and monitoring model performance in production environments.
Technical Architecture Planning
You will be responsible for planning the technical architecture of AI solutions. Practice designing scalable data pipelines and selecting appropriate infrastructure components that integrate with existing enterprise systems.
Stakeholder Communication
The role requires translating complex technical concepts for non-technical stakeholders. Practice explaining the business value, limitations, and risks of an AI model to a hypothetical domain expert or business leader.
04
Your preparation checklist
Prepare a project walkthrough focused on end-to-end deployment
Select a past project where you managed the full lifecycle from problem definition to production. Be ready to explain your technical architecture choices, how you collaborated with non-technical stakeholders, and how you utilized DevOps cycles to release the solution.
Research DNV's maritime and sustainability mission
Familiarize yourself with DNV's role as a ship classification society. Prepare to discuss how AI/ML can specifically improve maritime work processes, safety, or production digitalization, demonstrating your ability to identify business opportunities through technology.
Review your experience with cross-functional collaboration
Prepare specific examples of working within multi-national teams and collaborating with domain experts. Focus on how you translate complex technical findings into actionable insights for colleagues in different functional areas or geographic locations like Oslo.
Formulate strategic questions for the interviewers
Prepare thoughtful questions about the new AI team's current priorities in Pune, the specific DevOps tools used for productionizing models at DNV, and how the team balances experimental research with the need for practical, business-driven outcomes.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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