Principal Product Manager

Job Description

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 hyperscalersAWS, Azure, and Google Cloudand 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&aposll be responsible for defining and delivering the next generation of our contextualization tools. This isn&apost about traditional data integration; it&aposs about harnessing the power of AI to transform how industrial data, particularly engineering diagrams and unstructured documents, is understood and connected. You&aposll 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&aposll act as the key link between our engineering team in Bengaluru, our internal global stakeholders and our global customer base. You&aposll 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&aposs 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&aposll 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&aposll 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.

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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.