Quantifind’s risk screening and investigations platform harnesses the full potential of AI and external data to streamline AML-KYC from end to end. Backed by a16z and Redpoint.
About the role
As Technical Product Manager for Core Models, you will own the product direction for the models that power Graphyte’s risk intelligence. This role is co-sponsored by our Data Science organization. You will work closely with Product, Data Science, and Engineering to determine which model capabilities to build, where to improve existing models, and how to measure success. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
- 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
More about this role
You are a highly analytical professional that operates at the interface of Product Management and Data Science.. You understand that a model’s value depends only on how well it solves a real customer problem. You can translate a use case into a clear prediction target, define what constitutes a true positive, and reason through the tradeoffs between missed risk and unnecessary alerts. You are comfortable discussing precision, recall, and ROC analysis with data scientists, and explaining what those metrics mean to customers.
You are drawn to the intersection of product strategy and applied machine learning. You know how to turn customer needs into model requirements, evaluate whether an improvement matters in practice, and prioritize new capabilities alongside improvements to existing models. You are rigorous and analytical, but you move with urgency and make practical decisions with imperfect information.
You are collaborative by nature and have achieved success as a team player. You thrive by setting ambitious goals and tracking progress until they are met. You feel strongly about being mission-driven in your work, and above all else you are innately curious and motivated to...
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