Product Manager – AI
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Das ist der Job
Define product vision, product strategy, success metrics, and prioritization logic for one or more product streams.
Darum lohnt es sich
Manage collaboration with outsourced and remote development teams, keeping scope, quality, timelines, and product decisions transparent. Experience working with remote, distributed, or outsourced engineering teams.
Responsibilities Own the product lifecycle from opportunity discovery, problem framing, MVP definition, launch, analytics, iteration, and scale. Translate business goals and user problems into clear product hypotheses, scope, metrics, event tracking, user stories, and acceptance criteria.
Build, test, and iterate MVPs rapidly using user feedback, funnel data, cohort behavior, competitive signals, and AI-assisted research. Set up or specify product analytics: events, funnels, cohorts, activation metrics, retention metrics, experiment logic, and reporting needs.
Work closely with engineering, design, marketing, sales, support, and leadership across a remote/distributed setup. Use AI tools as part of daily product operations: discovery synthesis, competitor monitoring, PRD drafting, analytics exploration, QA support, prototype ideation, and productivity automation.
Requirements Several years in product management, preferably with early‑stage B2C, prosumer SaaS, creator/social, analytics, martech, or intelligence products. Proven experience launching MVPs or new product features from zero, with clear hypotheses, metrics, user feedback loops, and post‑launch decisions.
Strong product analytics skills: event taxonomy, funnel analysis, cohorts, A/B testing, activation/retention metrics, and tracking requirements. Ability to manage several product tracks or experiments without losing clarity on priorities, owners, decisions, and business impact.
Technical understanding strong enough to discuss APIs, data ingestion, tracking events, AI/LLM limitations, data quality, latency, edge cases, and implementation trade‑offs. Excellent written communication: clear PRDs, decision notes, experiment summaries, and stakeholder updates.
High ownership, high autonomy, and comfort working in ambiguity without waiting for perfect inputs. #J-18808-Ljbffr
Bereit?
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