Jobs at DataVisor

Lead the technical direction of a real-time fraud detection platform spanning streaming, storage, machine learning training pipelines, and AI-assisted engineering tools. Responsibilities include architecture, coding, code review, incident response, operational excellence, technical planning, mentoring, and cross-functional collaboration. The role requires hands-on development of distributed systems and establishing standards for safe, verifiable use of AI coding and agent tools.

Develop and productionize fraud detection machine learning models, engineer features from large-scale behavioral and transaction data, and monitor model performance. Lead complex fraud investigations by reconstructing attacker behavior, identifying patterns, producing technical reports, and translating findings into detection improvements and customer-facing fraud trend insights. Collaborate with product, engineering, operations, legal, executives, and customers.

Drive revenue growth through reseller, referral, marketplace, banking, payments, and fintech partnerships. Own partner-sourced pipeline, ARR, quota attainment, and channel sales performance. Build enablement programs, co-selling processes, joint go-to-market plans, and ecosystem playbooks while supporting opportunity qualification and closure. Translate fraud, AML, risk, and AI platform capabilities into partner and customer value propositions, collaborating with Sales, Product, Marketing, Legal, Solutions Engineering, and Customer Success.

Lead the technical direction of a real-time fraud detection platform, including streaming, storage, machine learning training pipelines, and distributed systems. Responsibilities include architecture, coding, incident support, technical planning, mentoring, operational excellence, and collaboration with product and customer-facing teams. The role also establishes standards for AI-assisted engineering and develops LLM- and agent-based tools for engineering operations.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Lead the technical direction and hands-on development of a real-time fraud detection platform, including streaming, storage, distributed systems, machine learning training pipelines, and AI-assisted engineering tools. The role drives architecture, technical planning, operational health, incident prevention, production support, and collaboration with Product and customer-facing teams. It also mentors engineers, establishes AI-assisted development standards, and evaluates LLM- and agent-based tools for engineering operations.

Build and operate production-grade AI, data, and distributed systems for fraud intelligence. Responsibilities include real-time data pipelines, high-throughput architecture, LLM-powered agentic workflows, AI tooling, rule and label automation, ML pipeline productionization, privacy safeguards, and cross-functional collaboration. The role requires strong Python engineering, distributed systems, cloud infrastructure, big data technologies, and machine learning fundamentals.

Manage and optimize GTM business systems and operational processes across Marketing, Sales, and Customer Success. Support HubSpot, CRM, marketing automation, integrations, data flows, and workflow automation. Build dashboards and executive reporting, analyze operational performance, improve data quality, and identify process efficiencies. Serve as a cross-functional resource for systems, reporting, and operational issues while using AI to enhance productivity and decision-making.

Manage a portfolio of customers as a trusted technical advisor for fraud and risk use cases. Oversee data integration and onboarding, troubleshoot platform performance, architect ML and rule-based solutions, lead deployments and business reviews, and coordinate cross-functionally to drive adoption and product improvements.

Lead and grow the Platform Engineering team to design, architect, and deliver a large-scale AI-driven fraud and risk decisioning platform. Set technical vision and roadmap, drive adoption of big data and real-time systems, collaborate with product and data science, enforce engineering rigor, and hire and develop senior engineering talent.

Design and build large-scale machine learning and real-time streaming systems to detect fraud using unsupervised, supervised, and deep learning; build pipelines using big data technologies and optimize distributed systems for real-time detection.

Serve as strategic partner to enterprise clients to drive ROI from an AI-powered fraud and AML SaaS platform. Lead onboarding, monitor detection performance, advise on ML and rule-engine strategies, coordinate with product and engineering, deliver business reviews, and educate customers on fraud/AML best practices to reduce risk and optimize operations.

Lead technical and strategic engagements in fraud detection, data analysis, and risk strategy development using DataVisor's platform. Collaborate cross-functionally and manage client projects for improved fraud prevention and operational efficiency.

Own DataVisor’s product marketing, PR, and analyst relations programs. Develop positioning, messaging, thought leadership, product assets, campaign materials, and executive communications while translating complex fraud, AML, AI, and SaaS technologies into customer-focused narratives. Collaborate across Product, Sales, Customer Success, Marketing, and leadership; manage agencies and analyst engagements; support launches and competitive intelligence; and use AI tools and agentic workflows to improve marketing productivity.

Serve as strategic Customer Success Manager for enterprise clients, driving ROI from fraud detection and AML platform use. Monitor detection performance, advise on ML and rule-engine tuning, coordinate with Product and Engineering, run business reviews, educate clients on best practices, and translate customer needs into product feedback to reduce fraud and streamline operations.

Serve as strategic partner and primary contact for enterprise clients, driving ROI from the fraud/AML SaaS platform. Advise on detection performance, ML-based models, rule engines, and best practices; coordinate with Product and Engineering; deliver business reviews, client education, and actionable feedback to reduce fraud and improve operations.

Serve as strategic partner to enterprise clients to drive ROI from fraud detection and AML solutions. Lead onboarding, monitor detection performance, advise on ML and rule-engine usage, coordinate with Product and Engineering, run QBRs, educate on best practices, and identify expansion and optimization opportunities.

As a Senior Technical Account Manager, you will manage client portfolios, provide technical guidance, oversee data integration, and ensure optimal platform usage for fraud detection.

The Software Engineering Manager will lead the Platform Engineering team, managing engineers while architecting an AI-based fraud detection platform utilizing big data technologies.

Develop machine learning systems to detect fraud in real-time, utilizing both supervised and unsupervised learning algorithms. Design scalable architecture and optimize data processing systems using big data technologies.