Note: The job is a remote job and is open to candidates in USA. Horizon3.ai is hiring a Staff Product Manager to lead the strategy, development, and evolution of NodeZero’s Agentic Data Platform. The role owns core data services and machine learning infrastructure, guiding platform reliability, security, cost strategy, adoption, and business outcomes.
Responsibilities
- Data Platform Product Strategy: Define and execute the product vision and multi-quarter roadmap for NodeZero’s data services and machine learning platform. Determine which capabilities should be centralized, evaluate build-versus-buy decisions, and align platform investments with business priorities, customer outcomes, and long-term product differentiation
- Database-as-a-Service (DBaaS) Ownership: Lead the evolution of managed relational, graph, and vector data services, including self-service provisioning, scaling, migrations, upgrades, and lifecycle management. Ensure data infrastructure delivers the reliability, performance, security, and flexibility required by internal teams and enterprise customers
- Asset and Attack Graph Infrastructure: Own the product strategy for NodeZero’s unified asset and attack graph, including complex data relationships, multi-hop query performance, data freshness, consistency, and scalable access to offensive security intelligence
- Data Architecture, APIs, and Integrations: Partner closely with Engineering to define scalable data models, query APIs, change-data-capture pipelines, event streams, schema governance, and platform abstractions that enable product, ML, and agent teams to build efficiently on shared infrastructure
- Machine Learning Platform Strategy: Own the infrastructure supporting traditional machine learning workflows, including training pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, deployment, monitoring, and evaluation standards. Enable teams to develop, deploy, and operate production ML models reliably
- Training Data and Data Quality: Define capabilities for curated, versioned, and documented training datasets derived from pentest and platform data. Drive data quality, lineage, reproducibility, labeling pipelines, ground-truth collection, and consistent offline and online features that support classification, entity resolution, risk scoring, prioritization, and anomaly detection
- Platform Reliability and Operational Excellence: Drive the capabilities required for high availability, scalability, disaster recovery, backup and restore, multi-region readiness, and operational resilience. Partner with Engineering to establish SLOs, data quality SLAs, observability, and performance metrics that ensure dependable platform services
- Data Security and Governance: Define product requirements for tenant isolation, hierarchical multi-tenancy, data access controls, encryption, retention, deletion, and sensitive security artifact handling. Ensure training data and machine learning workflows support contractual requirements, customer consent, anonymization, auditability, and model governance
- Infrastructure Cost and FinOps Strategy: Own the product requirements and business strategy for managing data and ML infrastructure costs. Develop investment business cases, evaluate total cost of ownership and ROI, partner with Finance on budgets and forecasts, and drive cost visibility, optimization, and accountability across teams, products, and tenants
- Business Ownership and Commercial Strategy: Own the platform’s business performance, including its contribution to ARR, retention, expansion, and gross margin. Identify opportunities for pricing, packaging, monetization, and differentiated customer value. Evaluate market opportunities, competitive positioning, and investment priorities to support long-term growth
- Platform Adoption and Customer Value: Drive adoption of shared data and ML capabilities across internal product and engineering teams, including migration from legacy solutions. Engage with customers and cross-functional stakeholders to understand requirements, improve activation and usage, and identify opportunities to deliver measurable customer value
- Cross-Functional Leadership and Execution: Collaborate closely with Engineering, ML Engineering, Design, Finance, Sales, Marketing, Customer Success, and executive leadership to translate product strategy into clear requirements, architecture decisions, launch plans, and measurable outcomes. Drive alignment, resolve trade-offs, and ensure successful delivery and adoption
- Performance and Metrics: Define and monitor KPIs including platform adoption, data availability, query performance, data freshness, model time-to-production, infrastructure reliability, cost per tenant, gross margin, retention, and revenue contribution. Lead regular business reviews and use operational, financial, and customer insights to guide prioritization and continuous improvement
Skills
- 8+ years of Product Management experience, including 4+ years building infrastructure, data, or machine learning platform products in B2B SaaS environments
- Deep technical understanding of production databases, including relational, graph, or similar systems, with the ability to engage engineers on replication, high availability, disaster recovery, indexing, and performance trade-offs
- Experience building or managing scalable data platforms, including data pipelines, data modeling, schema evolution, data contracts, APIs, and distributed data architectures
- Strong understanding of traditional machine learning infrastructure, including training data pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, and production model monitoring
- Experience defining data governance and security requirements, including lineage, anonymization, retention, deletion, tenant isolation, and responsible handling of sensitive data in multi-tenant SaaS environments
- Demonstrated financial acumen, including developing infrastructure cost models, evaluating total cost of ownership and ROI, building investment business cases, and partnering with Finance on cloud infrastructure spending
- Proven experience driving adoption of internal platforms, including establishing shared services, enabling engineering teams, and successfully migrating users from existing solutions
- Strong product judgment in balancing technical constraints, architectural decisions, reliability, customer needs, infrastructure costs, and long-term platform investments
- Ability to work credibly and collaboratively with senior engineering leaders, data scientists, and ML engineers to define requirements, evaluate trade-offs, and guide complex technical initiatives
- Strong business orientation with experience connecting platform investments to customer value, operational efficiency, adoption, revenue growth, and financial performance
- Excellent communication and stakeholder management skills, including the ability to influence executives, present investment recommendations, and translate complex technical concepts into clear business outcomes
- Comfort operating at both strategic and tactical levels, from platform vision and business strategy to PRDs, technical requirements, architecture reviews, prioritization, and launch execution
- Data-driven decision-making skills with experience defining operational and business metrics and using insights to drive product improvements
- Experience working in Agile environments and leading cross-functional initiatives in fast-paced, evolving organizations
- Previous experience as a database engineer, data engineer, ML engineer, Site Reliability Engineer (SRE), or technical founder
- Experience with security data platforms, including XDR, SIEM, exposure management, or offensive security products
- Hands-on familiarity with graph data modeling, entity resolution, vector databases, and semantic search
- Experience developing labeling pipelines, ground-truth datasets, or training data infrastructure
- Experience with FinOps practices, cloud infrastructure optimization, or FinOps certification
- Familiarity with MLOps technologies such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or Feast
- Understanding of generative AI data requirements, including embeddings, vector search, and fine-tuning datasets
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field
- An advanced degree in engineering, computer science, machine learning, or business is preferred
Benefits
- Equity package in the form of stock options (all full-time roles are eligible).
- Health, vision & dental insurance for you and your family.
- A flexible vacation policy.
- Generous parental leave.
- Numerous career development opportunities.
- A collaborative environment that encourages creativity and out-of-the-box thinking.
- A mix of remote and hybrid work models depending on role and location, including the Chicago office, where some roles require regular in-office presence.
Company Overview