Technical Product Manager - Core Models

Quantifind•Palo Alto, CA

2d•$145,000 - $185,000•Onsite

About The Position

Quantifind is seeking a Technical Product Manager for Core Models to own the product direction for the models that power Graphyte's risk intelligence. This role is co-sponsored by the Data Science organization and involves close collaboration with Product, Data Science, and Engineering to define model capabilities, improvements, and success metrics. The Technical Product Manager will translate customer needs and use cases into clear model objectives, guide model evaluation and threshold selection, drive model initiatives through delivery, and oversee ongoing model improvement based on feedback and performance monitoring. This role also involves co-owning customer-facing model documentation, serving as a customer-facing subject matter expert on models, and managing Quantifind’s interfaces with customer Model Risk Management processes. The position offers direct exposure to executive leadership and opportunities to innovate new solutions, working alongside innovative data scientists and engineers, with direct client exposure expected for gathering product insights.

Requirements

BS or MS in computer science, statistics, mathematics, engineering, or a related technical field, or equivalent practical experience
2+ years of product management, data science, and/or data analytics experience working with software powered by machine learning models or with significant model-driven capabilities
Strong working command of classifier performance metrics, including precision, recall, false-positive and false-negative rates, and ROC analysis
Ability to interpret evaluation results, explain threshold tradeoffs, and connect those tradeoffs to customer outcomes
Ability to turn an ambiguous customer problem into a well-defined model use case, prediction target, and measurable acceptance criteria
Understanding of how label quality, class imbalance, and the representativeness of evaluation data affect conclusions about model performance
Comfortable partnering closely with Data Science and Engineering, distinguishing product decisions from modeling implementation choices while contributing meaningfully to both discussions
Ability to communicate clearly with technical and nontechnical audiences and explain model capabilities and limitations without overstating what the evidence supports
Understanding of agile software development and ability to translate priorities into clear requirements and an actionable backlog
Ownership mentality, thrive in ambiguity, and build alignment by clearly communicating the “why” and driving toward measurable outcomes

Nice To Haves

Familiarity with natural language processing
Experience with financial crime or risk intelligence products
Exposure to model validation and Model Risk Management in regulated institutions

Responsibilities

Own the Core Models product strategy and roadmap
Prioritize model build and refresh initiatives based on customer needs, performance gaps, business value, and technical feasibility
Translate use cases into clear model objectives
Define prediction targets, what constitutes a true positive, and the performance requirements that reflect how customers use the results
Guide model evaluation and threshold selection
Partner with Data Science to establish evaluation criteria and select thresholds that balance precision, recall, missed risk, and review workload for the intended use case
Drive model initiatives through delivery
Define scope, requirements, acceptance criteria, and release priorities
Align Data Science and Engineering on execution
Assess results against the intended product outcomes
Guide ongoing model improvement
Use evaluation results, customer feedback, and performance monitoring to identify gaps and prioritize refreshes or further development
Co-own customer-facing model documentation with Data Science
Clearly explain intended uses, evaluation methods, performance, assumptions, limitations, and material changes
Serve as a customer-facing subject matter expert on our models
Support technical discussions, evaluations, and customer reviews, translating model behavior and performance into clear, defensible explanations
Own Quantifind’s interfaces with customer Model Risk Management processes
Coordinate model questionnaires, validation requests, supporting documentation, and responses to findings in partnership with Data Science and other internal stakeholders
Work directly with executive leadership as you help us innovate new solutions