Principal Technical Product Manager

Dir Data Science - GD06AEWe’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. Principal Technical Product Manager (AI & Data Science)Role OverviewWe are seeking a Principal Technical Product Manager to lead the strategy, development, and scaling of enterprise AI and data science products. This role sits at the intersection of product strategy, advanced analytics, and platform engineering, driving the end-to-end lifecycle from concept through production.As a senior individual contributor, you will operate as a product leader and technical translator, aligning stakeholders, shaping roadmaps, and ensuring delivery of high-impact AI solutions that deliver measurable business value.Key ResponsibilitiesProduct Strategy & VisionDefine and own the product vision, strategy, and roadmap for AI/ML-driven products and data platformsTranslate business objectives into scalable, reusable AI product capabilitiesIdentify opportunities to leverage advanced analytics, machine learning, and generative AI to drive business impactEnd-to-End Product OwnershipLead product lifecycle from opportunity identification → POC → MVP → production scalingEnsure products meet enterprise standards for architecture, governance, and complianceDefine success metrics (KPIs, adoption, ROI) and manage performance post-launchTechnical Product LeadershipPartner closely with engineering, data science, and platform teams to:Define technical requirements and architecture trade-offsPrioritize backlog and align delivery to business valueEnsure solutions are scalable, secure, and production-readyStay current on AI/ML trends and evaluate emerging technologiesStakeholder & Cross-Functional AlignmentAct as primary interface across business, technology, data science, and governance teamsLead workshops and facilitate alignment on priorities, scope, and trade-offsCommunicate clearly with both executive and technical audiencesExecution & Delivery ExcellenceDrive agile product development, including backlog prioritization and sprint planningManage dependencies across teams and ensure on-time, high-quality deliveryProactively identify risks and implement mitigation strategiesGovernance, Risk, and Responsible AIEnsure solutions adhere to enterprise AI governance, security, and compliance standardsEmbed best practices for model validation, monitoring, explainability, and fairnessPartner with risk, legal, and security teams on production readinessQualificationsExperience10+ years of product management experience, including:10+ years in technical product managementProven experience delivering data science, AI/ML, or analytics productsTrack record of shipping products from concept to production at scaleAdvanced understanding of adoption strategies and metricsTechnical SkillsStrong understanding of:Machine learning lifecycle (data prep, modeling, deployment, monitoring)Data platforms (cloud, data pipelines, APIs, analytics tools)AI/GenAI concepts (LLMs, prompt engineering, evaluation frameworks)Ability to engage deeply in technical conversations with engineers and data scientistsLeadership & ExecutionDemonstrated ability to lead cross-functional, matrixed teamsStrong prioritization and decision-making skills in ambiguous environmentsExcellent communication and stakeholder management skillsEducationMaster’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)Preferred QualificationsExperience building enterprise AI platforms or AI-enabled products, specifically in Gemini Enterprise Agent PlatformFamiliarity with cloud environments (GCP, AWS, Azure) and modern data stacksExperience with regulated industries (insurance, finance, healthcare)Key Skills for Regulated Industries: Regulatory & Compliance AcumenDeep understanding of regulatory frameworks (e.g., SOX, GDPR, HIPAA, Model Risk Management)Ability to translate regulatory requirements into product features, controls, and workflowsResponsible AI & Model GovernanceExperience implementing model validation, explainability, fairness, and bias detectionFamiliarity with AI governance frameworks, auditability, and documentation standardsAbility to operationalize model risk management (MRM) practices across the product lifecycleData Privacy & SecurityStrong understanding of data protection, PII handling, and data classificationExperience partnering with security teams on access controls, encryption, and data lineageKnowledge of secure AI/ML architectures (e.g., VPC controls, air-gapped environments)Audit & TraceabilityAbility to ensure solutions are fully auditable, with clear lineage from data → model → outputExperience supporting internal/external audits with appropriate controls and documentationEnterprise Risk ManagementAbility to identify and manage opera