Ambyint is a SaaS company with an Industrial IoT platform for artificial lift optimization. We combine physics-influenced AI models, closed-loop control and deep subject matter expertise to help oil and gas producers run wells autonomously across most lift types.
As a Product Manager, you own product outcomes across the products and initiatives you take on. You decide what we build and why, ship it with your squad, and prove it made a difference for customers. You work with a squad lead, engineers, data scientists, QA and design. Much of your work also spans several tech squads, such as Integration, Data Engineering and Dev Ops, and functions like Sales, CS, Marketing and Finance.
You drive the alignment that gets it delivered. Delivery tracking is part of the job, but your main value is picking the right problems and showing the result.
- Discovery and Strategy
- Run customer discovery with production engineers, operators and executives, including field visits
- Work with BD and Solutions Engineering on what wins and loses deals
- Prioritize by persona, job-to-be-done, customer value and lifetime value
- Bring your bets to quarterly roadmap planning
- Delivery and Execution
- Write clear specs: problem, goals, non-goals, acceptance criteria
- Own the backlog and partner with the squad lead on sprint planning; keep interrupts visible and elevate blockers early
- Drive alignment and coordinate dependencies across multiple tech squads and business functions
- Make trade-off calls with engineering, data science, QA and design
- Take part in hotfix post-mortems and fix root causes in what you own
- Manage funded programs when assigned, including grant deliverables, budget and reporting
- Measurement and Customer Value
- Set success criteria, KPIs and targets before work starts
- Check results after release and decide the follow‑up: product improvement, marketing campaign or CS engagement
- Express customer value in dollars: production uplift, failures avoided, labor saved
- Use product usage, customer health and deal data in every decision
- Launch and Communication
- Deliver release notes, CS and Sales enablement, and in‑app guides for what you ship
- Explain the roadmap by outcomes to CS, Sales and customers
- Report progress, risks and decisions to the VP of Product
- Discovery and Strategy: Your bets are backed by customer evidence, and you can show what you learned and who you learned it from.
- Delivery and Execution: Your squad delivers what it commits to, with specs clear enough to build without guessing, interrupts kept visible, and cross‑squad dependencies landing without surprises.
- Measurement and Customer Value: Every initiative has success criteria set before work starts and a review after release, with customer value expressed in dollars.
- Launch and Communication: CS and Sales are ready on launch day, and stakeholders know where your work stands without having to ask.
- Product management: Minimum 7 years in product management (not only project management) in B2B SaaS, ideally energy tech or industrial IoT.
- Proven outcomes: a shipped feature or product you can point to, the metric you set for it, and what happened to that metric.
- Customer discovery: you can run an interview and turn the notes into a clear problem statement.
- Data fluency: you can pull and read usage, customer and deal data, and reason about what ML model outputs mean for users.
- Clear writing: your specs and updates are short and specific.
- Cross-functional credibility: trusted by engineers and data scientists; able to explain the same work to a field operator and an executive.
- Agile delivery: Scrum or Kanban in Jira and Confluence.
- Travel: about 3–4 weeks per year to customer sites and the field.
- Assets: artificial lift…
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