Technical Product Manager - Core Models

Quantifind•Palo Alto, CA

1d•$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 ideal candidate will translate customer needs into model requirements, evaluate model performance, and prioritize new features alongside existing model enhancements. This position requires a rigorous, analytical, and collaborative individual who thrives on setting ambitious goals and is driven by a mission-oriented approach and a strong sense of curiosity. The Core Models product area is crucial for connecting model development with customer needs and workflows, ensuring customers understand the intelligence they rely on, including model assessments, performance measurement, and limitations. Quantifind offers a supportive environment that fosters collaboration, learning, and professional growth, along with a flexible work environment and a family-friendly work-life balance.

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, thrives in ambiguity, and builds 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, defining prediction targets, what constitutes a true positive, and performance requirements that reflect customer usage
Guide model evaluation and threshold selection in partnership with Data Science to establish criteria and balance precision, recall, missed risk, and review workload
Drive model initiatives through delivery by defining scope, requirements, acceptance criteria, and release priorities, aligning Data Science and Engineering on execution, and assessing results against intended product outcomes
Guide ongoing model improvement using 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 explaining intended uses, evaluation methods, performance, assumptions, limitations, and material changes
Serve as a customer-facing subject matter expert on our models, supporting technical discussions, evaluations, and customer reviews by translating model behavior and performance into clear, defensible explanations
Own Quantifind’s interfaces with customer Model Risk Management processes, coordinating model questionnaires, validation requests, supporting documentation, and responses to findings in partnership with Data Science and other internal stakeholders
Work directly with executive leadership to innovate new solutions
Validate assumptions, identify new opportunities, and become an expert on our product and customers
Gain direct client exposure, with the expectation that great product insights come from user feedback