Sr Product Manager

CollectivIQ is building an AI workforce platform for the enterprise. Our products give employees a team of AI specialists that can work across email, files and business systems; compare answers across more than 30 models from 10+ providers; build and host business applications from plain-language requests; and turn organizational knowledge into a governed, source-backed company memory.

The platform is designed for real work, not isolated experimentation. CollectivIQ combines Digital Direct Reports, multi-model Chat, Apps and Gigabrain with enterprise-grade identity, permissions, approvals, auditability and data controls. We are creating a new category of software that helps organizations move faster while maintaining trust, security and human oversight.

The Opportunity

CollectivIQ is hiring a Senior Product Manager to become one of the foundational members of its product and technology team. This person will help turn an ambitious platform vision into a focused roadmap, a repeatable product-development cadence and customer experiences that make sophisticated AI useful, understandable and safe for everyday work.

You will work closely with the VP of Data Science & AI, engineering leadership, design, go-to-market partners and customers. You will own meaningful product scope across the CollectivIQ platform and move comfortably between strategy and execution: discovering high-value workflows, writing clear requirements, making hard prioritization decisions, supporting delivery and measuring whether what we shipped created value.

This is not a maintenance role on a mature product. The right person enjoys creating clarity where the market, technology and customer expectations are changing quickly. You will be expected to learn fast, challenge assumptions and help establish how product work gets done at CollectivIQ.In this role, you will work closely on a daily basis with our distributed engineering and operations teams, primarily based in Peru. You will be a bridge between our domestic and international teams to ensure smooth alignment and execution

What You Will Own

Product strategy and roadmap: Translate company priorities and customer problems into a clear strategy, sequenced roadmap and measurable quarterly outcomes for assigned areas of the platform.

Customer discovery: Build a continuous discovery practice with enterprise users, administrators and buyers; identify repeatable workflows, unmet needs, adoption barriers and opportunities for differentiated value.

End-to-end product delivery: Lead initiatives from problem framing and solution discovery through requirements, build, launch, adoption and iteration in close partnership with engineering and design.

Product definition: Create decision-ready product briefs, user journeys, requirements, acceptance criteria and stories that give teams context without prescribing unnecessary implementation details.

Prioritization: Own and continuously refine the backlog, balancing customer value, speed to learning, technical dependencies, security, scalability, usability and platform economics.

AI product quality: Partner with technical leaders to define evaluation approaches for answer quality, task completion, groundedness, reliability, latency, safety and cost; ensure human review and approval are designed into consequential workflows.

Platform experience: Shape cohesive experiences across Digital Direct Reports, multi-model Chat, AI-built Apps, Gigabrain and enterprise connections rather than treating each capability as an isolated feature.

Enterprise readiness: Incorporate identity, permissions, role-based access, audit trails, data handling, administration and governance requirements into product decisions from the start.

Metrics and insights: Define and monitor a practical product scorecard, including activation, adoption, retention, successful task completion, quality, user trust, time saved, latency and cost per successful outcome.

Launch and enablement: Coordinate launch readiness and partner with Sales, Marketing, Customer Success and Implementation on positioning, demonstrations, pilots, training and customer feedback loops.

Market intelligence: Track model capabilities, agent platforms, enterprise AI products, customer expectations and regulatory or security trends; translate changes into thoughtful product choices rather than reactive feature chasing.

Communication and alignment: Make priorities, tradeoffs, decisions and progress visible to technical and nontechnical stakeholders, including company leadership.

Required Qualifications

Bachelor's degree in computer science, engineering, information systems, business, economics, product design, or a closely related field.

6+ years of product management experience, including ownership of technically complex B2B SaaS, enterprise software, data, developer platform, workflow automation, or AI-enabled products.

At least 5 years in a fast paced, start-up environment.

Cross-Cultural Communication: Proven experience collaborating effectively with distributed, international teams where English is spoken as a second language (ESL)

Demonstrated success taking products or major capabilities from discovery through launch, adoption, measurement and iteration.

Strong technical fluency and the ability to partner credibly with engineers and data or AI specialists on APIs, integrations, data flows, system constraints and architectural tradeoffs.

Experience translating ambiguous customer needs into clear product strategy, requirements, priorities and measurable outcomes.

A record of using qualitative research, product analytics and business data together to make decisions.

Excellent written and verbal communication, with the ability to explain complex concepts and tradeoffs to executive, technical and customer audiences.

Strong judgment, organization and ownership in an environment with changing information, competing priorities and limited structure.

Experience collaborating with distributed engineering teams in an agile or iterative product-development environment.

Preferred Qualifications

Master's degree in computer science, artificial intelligence, data science, engineering, information systems, or an MBA with a strong technology focus.

Hands-on product experience with generative AI, agentic workflows, model orchestration, retrieval-augmented generation, knowledge systems, AI evaluation or human-in-the-loop automation.

Experience defining quality metrics or evaluation datasets for probabilistic systems where traditional pass/fail testing is not sufficient.

Experience with enterprise identity, permissions, governance, auditability, privacy, security or regulated customer requirements.

Experience with integrations or ecosystems such as Microsoft 365, Google Workspace, Salesforce, Atlassian, GitHub, APIs or MCP servers.

Experience launching a zero-to-one product or building product practices within an early-stage or high-growth company.

Exposure to usage-based pricing, AI cost management, token economics or cloud consumption models.