Senior Product Manager

HatchWorks AI is an enterprise AI consulting firm that designs, builds, and operates production-grade AI and software systems for clients. The Senior Product Manager will own a product workstream within enterprise AI engagements, managing strategy, discovery, backlog, sprint cadence, stakeholder relationships, and adoption outcomes while delivering AI products for operational users.

Responsibilities:

• 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
• Outcome obsession — ship-feature is not a goal. Move-metric is. You distinguish between the two every day
• Ruthless prioritization — every sprint is a series of trade-offs. You make them, you defend them, you adjust them
• Operator empathy — you spend time with the people who'll use what you ship. Tribal knowledge becomes spec
• Delivery ownership — you own your workstream from discovery to handoff, in partnership with the engagement lead

Requirements:

• Enterprise PM — 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
• 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
• US-based. Some travel to client sites and HatchWorks office locations may be required by engagement
• Experience mentoring or supporting junior PMs
• Worked with knowledge graphs, semantic web, or rules-engine products
• Contributed to internal AI product playbooks or best practices