Principal Product Manager

Principal Product Manager, AI & Industrial Data Contextualization

contextualizing industrial data

batch production

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.

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: