Principal Product Manager

Principal Product Manager, AI & Industrial Data Contextualization

Cognite is building a new team in Bengaluru, India, focused on developing cutting-edge tools for contextualizing industrial data within our core platform, Cognite Data Fusion (CDF). We are seeking a Principal Product Manager to lead this strategic initiative, driving the vision, strategy, and execution for a product area at the intersection of industrial operations, data, and advanced AI.

Cognite Data Fusion is a foundational Industrial DataOps platform that runs on all major hyperscalers—AWS, Azure, and Google Cloud—and is deployed globally to many regions across the world. Our platform is used by a diverse range of customers spanning multiple industries, from those engaged in batch production and continuous production to companies in oil & gas and discrete manufacturing. Your work on contextualization will be crucial for empowering these customers, regardless of their industrial process.

Role Overview

As a Principal Product Manager, you'll be responsible for defining and delivering the next generation of our contextualization tools. This isn't about traditional data integration; it's about harnessing the power of AI to transform how industrial data, particularly engineering diagrams and unstructured documents, is understood and connected. You'll lead the charge in shifting from deterministic, rule-based systems to intelligent, self-correcting, and probabilistic AI-driven solutions that drastically reduce manual effort and improve data quality for our customers.

You'll act as the key link between our engineering team in Bengaluru, our internal global stakeholders and our global customer base. You'll work closely with customers to understand their challenges, define product requirements, and guide the team in building scalable, user-centric solutions. This role requires a blend of deep technical expertise in machine learning and a strong product management mindset focused on user outcomes and market needs.

Key Responsibilities

• Define Product Strategy: Develop and own the product vision and strategy for AI-driven contextualization, aligning it with Cognite's overall mission and market opportunities.
• Lead with Empathy: Engage with customers and internal stakeholders to deeply understand their problems, pain points, and workflows related to digitizing and connecting data.
• Translate Vision to Execution: Author detailed product requirements and user stories for the engineering team, ensuring a clear and shared understanding of goals. You'll be instrumental in prioritizing the product backlog to maximize impact.
• Navigate the AI Landscape: Drive the adoption of advanced machine learning techniques, including computer vision models (e.g., Faster R-CNN, YOLO, Siamese Networks), and other AI/ML approaches for symbol, text, and line detection in engineering diagrams.
• Champion a Shift in Mindset: Lead the transition from manual, deterministic methods to intelligent, probabilistic, and agentic workflows that can self-correct and optimize based on user-defined expectations rather than rigid, manual configurations.
• Manage Cross-functional Collaboration: Work closely with engineering, UX/UI, data science, and customer-facing teams to deliver a cohesive product experience. You'll also need to navigate the complexities of a multi-cloud environment, considering platform services from AWS, Azure, and GCP.
• Build the Team: Play a key role in hiring and mentoring product managers and other talent as the team grows in Bengaluru.

Required Skills & Experience

• Priority 1: AI-Driven Product Development & Market Launch: Proven experience in a product leadership role for AI/ML-driven products. You must have a demonstrated track record of launching successful AI products to market at scale. You should have a solid understanding of machine learning concepts, including model training, data sets, feature extraction, and different model architectures like convolutional neural networks.
• Priority 2: Industrial Data Expertise: Experience in the industrial sector is highly desirable. You should have a strong understanding of industrial data domains, including engineering data, sensor data, and data from ERP and MES systems. Familiarity with relevant industry standards is a significant advantage, including but not limited to:
• Engineering and Asset Data: DEXPI, CFIHOS, ISO 14224, ISO 15926
• Enterprise and Business Data: ISA-95, B2MML
• Product and Classification Data: ECLASS, UNSPSC, GPC
• Data-Centric AI Leadership: You have a proven track record of managing and leading teams that prioritize data quality and are structured for success with probabilistic models. This includes experience with:
• Data Collection & Labeling: Successfully managing the collection, curation, and labeling of huge volumes of complex data.
• Data-First Methodology: Implementing a test-driven AI/ML methodology using evaluation and benchmark datasets to drive continuous model improvement and measure performance.
• AI-Driven Configuration: A strong understanding of how to use AI to optimize product configurations based on user-described success metrics, moving beyond manual parameter tuning.
• Hands-on Experience with AI Projects: Direct experience leading projects involving computer vision and object detection, ideally on complex industrial imagery or technical diagrams. Knowledge of techniques for synthetic data generation and fine-tuning models on limited, proprietary data is a significant advantage.
• Product Management Acumen: A minimum of 8 years of product management experience, with a track record of successfully bringing complex technical products to market.
• Cloud Computing and Multi-cloud Solutions: Experience with cloud platforms such as AWS, Azure, and/or Google Cloud. A strong understanding of how to build and manage products that can be deployed across different cloud providers is essential. Relevant services include:
• AWS: SageMaker, Lambda, Bedrock Data Automation, Textract
• Azure: Azure Machine Learning, Azure Functions, Azure AI Document Intelligence
• GCP: Google Cloud Vertex AI, Google Cloud Functions, Google Cloud Document AI
• Technical Communication: The ability to communicate complex technical concepts to both technical and non-technical audiences, translating AI capabilities into tangible business value.

Strategic Thinking: The ability to think beyond current solutions and envision a future where AI radically simplifies industrial data management. You should be able to define what "good" is and establish metrics to measure success.