Quantifind sells SaaS risk intelligence that mines unstructured text for financial-crime signals, serving banks and government agencies. Its stack pairs Scala, Spark and Hadoop data pipelines with Kubernetes on AWS, including GovCloud, which points to regulated public-sector deployments. Senior-heavy, engineering-led hiring that is accelerating lines up with projects in entity resolution, knowledge graphs and Graphyte proofs of concept.
Notable leadership hires: Customer Success Director
Quantifind is a privately held data science company founded in 2009 and headquartered in Palo Alto, California, with 51–200 employees. Its AI platform combines a financial institution's internal data with public-domain sources to assess risk for KYC, customer due diligence, fraud risk management and anti-money laundering programs. Customers include large banks and government agencies investigating criminal networks and election interference. The product is delivered as SaaS, and the company hires in the United States and Canada.
Quantifind uses Scala, Python, Java and Go with Apache Spark, Hadoop and Airflow. It runs on Kubernetes and Docker across AWS EKS, ECS, RDS and GovCloud, with Terraform/OpenTofu, Jenkins and PostgreSQL.
Current work includes Spark pipelines for risk signal generation, entity resolution for financial crime detection, AML document classification, knowledge graphs for risk analysis and proofs of concept on its Graphyte platform.
Quantifind is headquartered in Palo Alto, California. It hires in the United States and Canada, mostly senior engineering roles, plus support, data and customer success positions.
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Quantifind's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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