Lead Product Manager

Role Overview

• Own end-to-end product delivery for an assigned platform area — legacy modernization, data infrastructure, integrations, or cross-cutting platform work.
• Build credibility with Engineering by shipping.
• Write specs technical enough for Engineering to execute with confidence — clear data models, well-scoped requirements, and documented trade-off decisions.
• Serve as the connective tissue between Engineering, Data Science, Customer Success, and leadership — translate the complex for both technical and non-technical stakeholders, including at the executive level.
• Use SQL and data tooling independently for analysis and discovery; evaluate API specs without engineering translation; bring quantitative rigor to prioritization.
• Own Tango's AI/data product strategy across all segments — retail site intelligence, corporate real estate, energy & sustainability — and build the roadmap to deliver it.
• Directly PM Tango's ML team — own the ML product roadmap, shape requirements for ML-powered features, evaluate model capabilities and limitations, and define success metrics grounded in measurable user outcomes, not model accuracy alone.
• Own AI evaluation standards: define accuracy thresholds, hallucination tolerance, latency SLAs, and human-in-the-loop requirements before any capability ships to production.
• Drive customer discovery with rigor — maintain active design partner relationships, document what changes roadmap direction, and build validated signal from real usage, not just stakeholder opinion.
• Anchor the AI/data roadmap to measurable customer outcomes — adoption, retention impact, time-to-value.
• Implement enterprise-grade AI guardrails: confidence thresholds, audit trails, and citation standards appropriate for enterprise customers with compliance obligations.
• Identify and evaluate integration and partnership opportunities that extend Tango's platform value, particularly where AI or data capabilities are the differentiator.
• Frame the business case for AI/data investment in customer outcome terms — retention, expansion, time-to-value — not technology for its own sake.

Requirements

• 7+ years in product management with a demonstrated track record on technical, data, or platform products in a B2B SaaS environment.
• Proven ability to drive delivery on complex, cross-functional technical programs: must have a shipping track record, not just a strategy track record.
• Has shipped AI or ML features to production — not prototypes or internal tools — and iterated based on real usage data, not stakeholder opinion.
• Experience building trust and influencing Engineering and Data Science teams through technical credibility and clear product decisions, not just communication skills.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field — or equivalent experience.
• Deeply technical PM: comfortable reading data models, writing SQL for analysis, evaluating API specs, and parsing engineering trade-offs independently.
• Fluent in LLM-specific product patterns: RAG, evaluation frameworks (offline evals, regression testing, feedback loops), prompt versioning, latency/cost trade-offs, and human-in-the-loop design.
• Working knowledge of data infrastructure concepts: pipelines, schemas, data quality, API integration patterns, multi-tenant data environments, and BI tooling.
• Uses AI tools actively in daily workflow — synthesis, research, spec writing — and can evaluate AI tooling for product relevance and applicability.

Tech Stack

• SQL

Benefits

• Comprehensive Benefits Including health, dental, and vision insurance
• 401(k) plan with company match
• Generous paid time off to support your well-being
• Flexible Work Environment Whether remote, hybrid, or in-office