Product Manager – Data & AI Products

About Crayon Data

Crayon Data is an AI-first company headquartered in Singapore, with teams in India and the UAE. Since 2012, weve built data and AI platforms that simplify decisions and drive enterprise revenue.

Were now building Tangram.ai - a modular, GenAI-powered enterprise platform that enables organizations to assemble AI agents, models, and solutions securely and at scale.

Why join us?

Crayon is evolving into an AI-native organization, where data, GenAI, and product thinking come together.

As a Product Manager (Data & AI) , youll work at the intersection of business, data, and engineeringshaping AI-driven products from problem definition to scalable delivery. Youll define product strategy, translate enterprise use cases into clear roadmaps, and work closely with data science, engineering, and GTM teams.

You wont just manage features youll build data- and GenAI-powered products that directly impact customer outcomes and revenue.

Key Responsibilities

1. Product Strategy, Discovery & Execution

• Own problem discovery across enterprise clients, internal stakeholders, and product usage data.
• Define product outcomes, success metrics, and clear hypotheses before building.
• Own the product roadmap, prioritizing based on impact, feasibility, and data signals.
• Write clear PRDs, user stories, and acceptance criteria in Jira, Notion, or equivalent tools.
• Partner with engineering and design teams in agile sprints to ensure high-quality, on-time delivery.

2. Data Analytics & Data-Informed Decision Making (New, Explicit)

• Define and track core product KPIs, adoption metrics, and feature usage patterns.
• Use dashboards, SQL, or analytics tools to analyze funnels, cohorts, and behavioral trends.
• Perform basic exploratory data analysis (plain meaning, looking at data to spot patterns and anomalies).
• Ensure features are instrumented correctly for measurement and learning.
• Translate data insights into clear product decisions, trade-offs, and leadership updates.

3. AI-First Product Thinking & Basic Data Science Literacy

• Design AI-native product capabilities using LLMs, recommendation systems, automation, and data enrichment.
• Understand core data science concepts such as:
• Features, labels, training data, inference (how models learn and make predictions)
• Precision, recall, explainability, and model drift (accuracy, transparency, and performance decay)
• Collaborate effectively with data scientists to:
• Frame business problems into ML-suitable problem statements
• Evaluate model outputs, limitations, and risks
• Ensure responsible and explainable use of AI in client-facing workflows.

4. Rapid Prototyping & Experimentation Using AI Tools

• Rapidly prototype product ideas using AI tools, no-code/low-code platforms, or prompt-based workflows.
• Create mock user journeys, conversational flows, and recommendation logic to validate ideas early.
• Use prototypes to align stakeholders, test feasibility, and reduce engineering rework.
• Run small, focused experiments and iterate based on data and feedback.

5. Enterprise Client Management & Solutioning

• Act as the primary product partner for enterprise clients from discovery through delivery.