We're looking for a Senior Product Manager to own the product for a workstream inside one of our enterprise AI engagements — across forecasting, optimization, knowledge engineering, or user-facing interfaces. You will own the backlog, sprint cadence, and the adoption outcomes for that workstream, partnering with a Principal Product Manager or engagement lead on the broader program.
You will work directly with client stakeholders and a delivery team of 5+ engineers and designers, making trade-offs daily about scope, sequencing, and what's worth shipping in the next sprint. If you've shipped AI products that actually move metrics — this is the role.
WHAT YOU'LL DO
- Own product strategy for your workstream inside a multi-quarter enterprise AI engagement — from discovery through steady-state.
- Own adoption success criteria for your workstream — the structured measures that determine whether the system is actually being used, not just shipped. You instrument them and report on them to the engagement lead.
- Run discovery — operator interviews, SME workshops, behavior-pattern analysis. Convert tribal knowledge into shippable requirements.
- Contribute to structured-feedback taxonomies with users and SMEs — the vocabulary that turns every user override into a model-retuning signal.
- Manage the backlog for your workstream — within forecasting, optimization, knowledge engineering, or user-facing UIs. Sequence ruthlessly.
- Day-to-day contact for your workstream — regular working relationship with business leads, SMEs, and operators.
- Partner with the client's product counterpart on your workstream — build trust, share context, support ownership transfer.
- Run sprint cadence — two-week sprints, sprint reviews, and reporting into the engagement's steering cadence.
- Trade off ruthlessly — cut scope that doesn't move the metric. Defer complexity until the data rewards it. Right-size investment.
- Flag Phase 2 opportunities to the engagement lead — additional scope, new use cases within your workstream. The engagement doesn't end; it converts.
- Operate with consulting rigor — build credibility fast with new stakeholders, adapt your working style to each client's culture and tools, and turn ambiguity into a structured recommendation you can defend in the room.
- You'll execute with clarity and pace — the engineers and designers on your workstream take cadence from you.
Note: US-based. Some travel to client sites and our office locations may be required by engagement.
WHAT WE NEED FROM YOU
You will be expected to execute hands-on technical work from day one. The requirements below reflect the actual skills needed to deliver outcomes for enterprise clients.
Must-Haves
● Enterprise Product Management — 5+ years shipping products inside mid-size to Fortune 500 enterprises
● AI / ML Product — 2+ years working on AI products in production — recommendation systems, forecasting, optimization, NLP
● Operational Software — Built products for operational users (planners, schedulers, ops teams) — not consumer apps
● Stakeholder Range — Comfortable engaging business leaders and individual operators in the same week
● Backlog & Cadence — Run sprint-level delivery for a workstream; defensible prioritization under pressure
● Data Literacy — Read model outputs, understand calibration, interpret optimization results
● Discovery Practice — Experience running structured discovery — interviews, shadowing, workshop facilitation
● Vendor / Partner Coordination — Comfortable in multi-vendor environments — SI partners, internal platform teams
● Consulting Experience — Proven track record working as an external consultant or in a client-services model — building trust quickly, managing ambiguity, and adapting across client environments
Core Tech Stack — Tools & Methods
● Methods — Continuous discovery · Outcome-based roadmapping · RICE / WSJF prioritization
● Cadence — Scrum · Shape Up · Adapted hybrid models
● Tools — Notion · Linear / Jira · Miro / FigJam · Loom
● Analytics — Mixpanel · Amplitude · SQL competence
● Adjacent — Figma literacy · Comfort reading code and reviewing PRs
NICE TO HAVE
● Experience mentoring or supporting junior Product Managers
● Worked with knowledge graphs, semantic web, or rules-engine products
● Contributed to internal AI product playbooks or best practices
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