Job description

Who You Are 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 learn new things. Who We Are Quantifind is the Risk Intelligence company. We help some of the world’s biggest banks identify money laundering and fraud. Quantifind also works with government agencies, using the same platform, to uncover criminal networks and support national security. Unlike other players in this space, Quantifind delivers results in a software-as-a-service (SaaS) model with consumer-grade user experiences. Quantifind’s Graphyte platform uncovers signals of risk across disparate and unstructured text sources using artificial intelligence and natural language processing techniques. In financial crimes risk management, Quantifind’s solution combines internal financial institution data with public domain data to assess risk in the context of Know Your Customer (KYC), Customer Due Diligence (CDD), Fraud Risk Management, and Anti-Money Laundering (AML) processes. Our customers need to understand the intelligence they rely on: what our models assess, how their performance is measured, and where their limitations lie. The Core Models product area connects the development of our models to those customer needs and the workflows they support. To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family-friendly work-life balance. What A Great Candidate Looks Like: BS or MS in computer science, statistics, mathematics, engineering, or a related technical field, or bring 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. You have a strong working command of classifier performance metrics, including precision, recall, false-positive and false-negative rates, and ROC analysis and can interpret evaluation results, explain threshold tradeoffs, and connect those tradeoffs to customer outcomes. You can turn an ambiguous customer problem into a well-defined model use case, prediction target, and measurable acceptance criteria. You understand how label quality, class imbalance, and the representativeness of evaluation data affect conclusions about model performance. You are comfortable partnering closely with Data Science and Engineering, distinguishing product decisions from modeling implementation choices while contributing meaningfully to both discussions. You communicate clearly with technical and nontechnical audiences and can explain model capabilities and limitations without overstating what the evidence supports. You understand agile software development and can translate priorities into clear requirements and an actionable backlog. You have an ownership mentality, thrive in ambiguity, and build alignment by clearly communicating the “why” and driving toward measurable outcomes. Bonus: You bring familiarity with natural language processing, experience with financial crime or risk intelligence products, or exposure to model validation and Model Risk Management in regulated institutions. The Opportunity We Offer 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. In this role, you will: 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; and 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. Our team of highly innovative data scientists and engineers will challenge you to validate assumptions, identify new opportunities, and become an expert on our product and customers. You will have direct client exposure, with the expectation that great product insights come from user feedback. Principally, you will become part of a motivated and curious product team on a journey to create the world’s premier risk intelligence platform. Location Quantifind’s development team is centered in Palo Alto, California, and we have hubs in Atlanta, Georgia, New York, NY, Boston, MA and in Washington, D.C. Quantifind is currently using a hybrid mix of working from home and in the office, with regular in-person touchpoints and shared office space. This role is In-Person at our Palo Alto, CA office. A Highlight of Our Benefits Competitive salary Company equity Exceptional benefits package Flexible vacation & paid time off Employer-matched 401(k) plan A fun environment where work-life balance is valued The base salary range for this full-time position is $145,000 - 185,000. Our salary ranges are determined by role, level, and location, and the range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location, role-related knowledge and skills, depth of experience, relevant education or training, and additional role-related considerations. Depending on the position offered, equity, bonuses, commission, or other forms of compensation may also be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits. We are an equal opportunity employer. We pride ourselves on living our values. We are curious. We respect each other. We are proactively transparent. We relentlessly solve problems. We win together. Will you join us? Apply now!

Requirements

Must-haves

BS or MS in computer science, statistics, mathematics, engineering, or a related technical field, or bring 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.
You can turn an ambiguous customer problem into a well-defined model use case, prediction target, and measurable acceptance criteria.
You understand how label quality, class imbalance, and the representativeness of evaluation data affect conclusions about model performance.
You are comfortable partnering closely with Data Science and Engineering, distinguishing product decisions from modeling implementation choices while contributing meaningfully to both discussions.
You communicate clearly with technical and nontechnical audiences and can explain model capabilities and limitations without overstating what the evidence supports.
You understand agile software development and can translate priorities into clear requirements and an actionable backlog.
You have an ownership mentality, thrive in ambiguity, and build alignment by clearly communicating the “why” and driving toward measurable outcomes.
The Opportunity We Offer
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.
Work directly with executive leadership as you help us innovate new solutions.
A Highlight of Our Benefits
Competitive salary
Company equity
Exceptional benefits package
A fun environment where work-life balance is valued
We are an equal opportunity employer. We pride ourselves on living our values. We are curious. We respect each other. We are proactively transparent. We relentlessly solve problems. We win together.
Will you join us? Apply now!

Nice-to-haves

Bonus: You bring familiarity with natural language processing, experience with financial crime or risk intelligence products, or exposure to model validation and Model Risk Management in regulated institutions.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningagile

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

401(K)

Paid Time Off