Confirmed Product Manager (B2B SaaS Data & AI) - vdd.ai
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- Dubai
- Permanent
- Full-time
- Own product discovery and delivery for B2B SaaS Data & AI products (e.g., AI Pricing co-pilot, data/analytics modules, enterprise integrations).
- Assist the founders to define product vision, strategy, and roadmap aligned with company objectives (profitability, efficiency, adoption, retention).
- Translate customer needs into clear PRDs, user stories, acceptance criteria, and prioritised backlogs—balancing business value, feasibility, and time-to-market.
- Partner with Engineering, Data, and ML teams to scope and deliver data-intensive and AI-driven features (pipelines, model outputs, monitoring, governance, explainability).
- Drive product discovery: customer interviews, problem framing, workflow mapping, competitive analysis, and quantitative insights from product usage data.
- Design end-to-end user experiences for enterprise workflows (admin, permissions, configuration, integrations, reporting), ensuring usability and trust in AI outputs.
- Own success metrics and instrumentation: define KPIs/OKRs, ensure events tracking, analyse funnels, and iterate based on adoption and impact.
- Coordinate cross-functional execution: align stakeholders, run sprint rituals when needed, unblock delivery, and maintain crisp communication across teams.
- Work closely with Sales/Customer Success on enterprise deployments: onboarding, enablement, feature positioning, feedback loops, and roadmap communication.
- Contribute to go-to-market readiness: packaging, pricing inputs, release notes, demos, and sales collateral for product launches and iterations.
- Degree in Computer Science, Engineering, Business, or related fields (or equivalent experience).
- Proven track record delivering B2B SaaS products with measurable business impact (adoption, revenue, retention, efficiency).
- Strong product craftsmanship: PRDs, prioritization frameworks, roadmapping, and stakeholder management in fast-moving environments.
- Fluency with data/AI product fundamentals: metrics, experimentation, model lifecycle concepts, data quality, and AI/ML constraints.
- Excellent analytical skills: ability to define and interpret KPIs, structure ambiguous problems, and make data-informed decisions.
- Ability to communicate complex technical concepts clearly to non-technical stakeholders—and business context clearly to technical teams.
- Hands-on understanding of modern data ecosystems and enterprise integrations (APIs, ETL/ELT concepts, data warehouses like Snowflake/Databricks, cloud basics).
- Experience in enterprise environments (security, compliance, governance, permissions, SLAs) is highly appreciated.