Top Tech Jobs & Startup Jobs

Leads and develops a fraud analyst team responsible for investigating emerging threats, responding to incidents, and improving fraud-risk strategies. Owns investigative standards, playbooks, workflows, and tools while serving as an escalation resource for complex cases. Partners with Data Science, Customer Success, Product, Engineering, and business leaders to convert fraud insights into scalable signals, model improvements, and better customer outcomes. Supports customer conversations as a fraud subject-matter expert and coaches analysts on communicating risk decisions.

Senior Commercial Counsel will draft, review, and negotiate complex subscription, partnership, vendor, and other commercial agreements. The role primarily supports a global Sales organization, advises cross-functional teams, manages contracts managers, and handles enterprise and strategic partnership transactions. Responsibilities include balancing competing priorities, supporting legal projects, and providing practical, business-minded legal guidance in a fast-paced environment.

Engineering Interns contribute to software features across the full development lifecycle, including design, development, testing, deployment, monitoring, and improvement. They work with APIs, databases, streams, and queues while participating in stand-ups and peer-led teams. The role provides mentoring and hands-on experience with Signifyd’s technology stack. Candidates should be pursuing a bachelor’s degree in computer science or software engineering, show interest in software development and machine learning, and be curious, collaborative, and willing to learn.

Analyze and reconcile chargeback files across payment processors, investigate fraud, determine liability, process merchant reimbursement claims, gather transaction evidence, and dispute eligible cases. Support merchant onboarding, account configuration, policy compliance, quality assurance, and process improvements while collaborating with Customer Success, Product, Finance, banks, processors, and customers.

Provides post-sales technical account management and support for Latin American merchants. Responsibilities include leading e-commerce integrations, troubleshooting API and platform issues, coordinating escalations with internal teams, managing implementation milestones, maintaining CRM updates, training merchants, documenting best practices, and advising customer success and product teams. The role requires strong SaaS, e-commerce, API, technical support, communication, and stakeholder-management skills, plus fluent English and Spanish.

Investigate and process credit card chargebacks, reconcile processor files, determine liability, recover funds, and mitigate fraud losses. The role involves applying coverage policies, validating transaction evidence, onboarding and reviewing merchant accounts, troubleshooting data discrepancies, and collaborating with customers, banks, processors, Finance, Product, and Customer Success. Advanced Excel, payment processor, card network, research, and analytical skills are required.

Leads and develops an engineering team building, deploying, and operating machine-learning models and services for fraud prevention. Owns roadmap delivery, technical decisions, reliability, testing, monitoring, and stakeholder communication. Partners with data scientists, ML engineers, product, risk, and engineering teams. Drives responsible AI-tool adoption, recruits and coaches engineers, manages priorities, and ensures scalable, high-quality production systems.

Build and deploy production machine learning models and fraud decisioning tools. Partner with business teams to identify performance gaps, automate repetitive work, run experiments, and optimize transaction-risk strategies. Write production and analytical Python code, use SQL and distributed data platforms such as Spark, Databricks, and GCP, and communicate findings to technical and business audiences. Participate in code reviews, peer collaboration, and an on-call weekend rotation approximately six times annually.

Build and deploy production machine learning models and fraud decisioning tools for e-commerce transactions. Partner with business teams to improve decisioning performance, automate manual processes, run experiments, and balance model complexity with performance. Write and review Python and SQL code, work with distributed data pipelines using Spark, Databricks, and GCP, communicate findings to technical and business audiences, and participate in an on-call weekend rotation.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.