Senior Product Manager

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