Senior Manager, Product Management - Text Analytics

Qualtrics Qualtrics · Seattle · Seattle, WA · Product

Senior Manager, Product Management for Text Analytics at Qualtrics. This role leads the strategy and execution to evolve the company's text analysis engine from classical ML/rules-based structures to a hybrid architecture leveraging LLMs for enterprise scale. The role involves managing a team of product managers, partnering with applied science and engineering, and influencing how clients understand unstructured data. Key responsibilities include defining a multi-year hybrid roadmap, scaling AI responsibly with evaluation frameworks, improving existing ML systems, leading product managers, and aligning cross-functional strategy. Requires proven PM leadership, AI/ML product delivery experience (NLP, text analytics, ML, AI-powered enterprise software), technical fluency in classical ML/NLP and LLM mechanics, and experience with cost/latency/performance modeling for ML/LLM systems.

What you'd actually do

  1. Define and execute a multi-year hybrid roadmap that seamlessly blends classical NLP precision with the reasoning power of LLMs across all Qualtrics product lines.
  2. Design and implement rigorous evaluation frameworks and quality benchmarks to balance model accuracy against latency, cost, and enterprise reliability.
  3. Continuous investment in existing rules-based and ML systems to steadily improve topic detection, sentiment analysis, and maintainability for legacy clients.
  4. Lead, scale, and establish clear operational priorities for a team of product managers covering various portfolios within text analytics.
  5. Partner with engineering, applied science, data privacy, and product marketing to transform complex models into shippable, compliant, and highly marketable features.

Skills

Required

  • Proven PM Leadership (>3 years managing, coaching, and developing other product managers)
  • AI/ML Product Delivery (building and shipping NLP, text analytics, machine learning, or AI-powered enterprise software at scale)
  • Technical Fluency (classical ML/NLP techniques, modern LLM mechanics)
  • Strategic Trade-off Management (cost, latency, and performance models for ML/LLM systems)

Nice to have

  • Experience with LLMs for enterprise scale
  • Experience with hybrid architectures blending classical ML and LLMs
  • Experience with prompt engineering
  • Experience with fine-tuning
  • Experience with RAG
  • Experience with evaluation metrics for LLMs

What the JD emphasized

  • Balancing model accuracy against latency, cost, and enterprise reliability
  • rigorous evaluation frameworks and quality benchmarks
  • classical NLP precision with the reasoning power of LLMs
  • cost, latency, and performance models for large-scale production deployments of ML or LLM systems
  • Deep understanding of classical ML/NLP techniques (topic modeling, classification) and modern LLM mechanics (prompting, fine-tuning, RAG, and evaluation metrics)

Other signals

  • leading product strategy for text analytics
  • evolving mature engine into cutting-edge hybrid architecture leveraging LLMs
  • leading a team of product managers
  • partnering with applied science and engineering
  • influencing how clients understand open-ended feedback, conversations, and unstructured data