Job description

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

Requirements

Must-haves

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.
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.
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.
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Effort: medium