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Tempo is seeking a Senior Product Manager to lead Workforce Intelligence, an emerging product that helps enterprises measure and manage the value of AI-powered work. With over 30,000 customers including a third of Fortune 500 companies, Tempo is the #1 time management add-on for Jira and a trusted name in the Atlassian ecosystem.
Workforce Intelligence addresses a critical gap: while engineering organizations invest heavily in AI tools, they lack visibility into what those investments deliver. The product connects AI telemetry to Jira work items, epics, and initiatives to become the execution-layer system of record for AI return on investment. This is an opportunity to define an emerging category and shape how enterprises evaluate AI tools, benchmark models, and govern agent-only work.
You will own the strategy, roadmap, and commercial outcomes for Workforce Intelligence, measured by adoption, retention, and revenue rather than feature volume. You'll work directly with engineering leaders, CTOs, and VPs of Engineering to understand how they evaluate AI tools, productivity, spend, and risk. Your responsibilities include:
- Developing and executing the Workforce Intelligence strategy and roadmap with clear bets tied to activation, adoption, retention, and revenue
- Running continuous discovery with technical buyers and design partners to validate customer problems
- Turning uncertain problems into testable hypotheses, prototyping quickly, and running experiments with real customer data
- Leading the evolution of trusted attribution and reporting, from AI activity and cost at the work-item level to executive narratives about throughput, delivery dates, and capacity
- Developing Tempo's point of view on agent oversight, benchmarking, and tool evaluation with transparent methods
- Setting the connector and data strategy across AI providers, developer environments, source control, and collaboration tools
- Partnering with Product Managers for Timesheets, Capacity Planner, and Financial Manager to create connected customer value
- Working with sales, marketing, and customer success on positioning, pricing, packaging, launches, and enablement
- Defining instrumentation and operating metrics, reviewing evidence regularly, and making visible trade-offs
- Representing Workforce Intelligence with customers, partners, Atlassian, and senior leaders
Success in the first year means shipped attribution and reporting that VPs of Engineering trust enough for board conversations, a validated point of view on agent governance tested against customer data, a portfolio of experiments that killed at least one assumption and replaced it with data-supported insights, and becoming the trusted source of truth for what AI delivers.
You'll work in small, dedicated teams with minimal handoff and maximum ownership. The team uses AI collaboratively throughout their own work to streamline communication and accelerate delivery.
REQUIREMENTS:
Product and Business Experience:
- 5+ years of product management experience, including end-to-end ownership of a B2B SaaS or data product for a technical audience
- A record of improving adoption, retention, expansion, or revenue, with ability to explain decisions, measures used, and results achieved
- Experience taking a product or major product area from discovery through launch and growth, working closely with go-to-market teams, engineering, and design
- Strong judgment in ambiguous markets; ability to separate interesting capabilities from customer problems worth solving and make clear investment cases
Discovery and Experimentation:
- Deep customer discovery skills across interviews, usage data, prototypes, and live product experiments
- Experience designing tests that can disprove a product hypothesis, not just collect positive feedback
- Comfort working with imperfect or inferred data; ability to surface confidence and limitations clearly and avoid presenting correlation as proof
- Discipline to change a roadmap when customer evidence conflicts with the original plan
Technical and Domain Fluency:
- Experience building developer tools, engineering intelligence, data and analytics products, AI governance products, or another technically complex enterprise product
- Working knowledge of software delivery: Jira work items, source control, pull requests, engineering performance measures, and how AI tools and agents enter that workflow
- Data fluency to partner closely with engineering and analytics on attribution models, instrumentation, benchmarks, and reporting methodology
- Experience with the Atlassian ecosystem or another platform and marketplace business is a strong advantage
AI Product Judgment:
- Hands-on use of modern AI tools and a tested point of view on where AI produces measurable value and where it can create convincing but unreliable output
- Understanding of product trade-offs around model evaluation, agent behavior, privacy, governance, cost, and rapidly changing vendor capabilities
- Ability to define a trustworthy measure of human and AI-assisted work, including where evidence supports a conclusion and where it does not
Communication and Complexity Tolerance:
- History of aligning engineering, design, sales, marketing, customer success, finance, and executives around shared outcomes without direct authority
- Clear, concise writing and presentation skills; ability to explain complex technical and commercial problems without jargon
- Credibility with senior technical buyers and curiosity to learn how their decisions are actually made
- Low attachment to own ideas and high commitment to outcomes; ability to share hypotheses early, invite challenge, and make decisions plainly
