Top Tech Jobs & Startup Jobs

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.

Generate qualified pipeline for high-value e-commerce accounts through targeted research, personalized outreach, discovery calls, and collaboration with strategic sellers. Manage mid-funnel opportunities, maintain accurate CRM records, coordinate with Marketing, Sales Operations, and Partnerships, report on sales metrics, and contribute to sales process improvements. The role requires strong communication, organization, CRM proficiency, and understanding of longer sales cycles.

Own strategic enterprise customer relationships from onboarding through growth, ensuring platform performance, alignment, retention, and expansion. Build executive stakeholder relationships, lead discovery and change-management sessions, resolve issues cross-functionally, create long-term growth plans, conduct business reviews, identify upsell and cross-sell opportunities, and collaborate with Product, Marketing, Sales, Implementation, and Risk teams. Travel approximately 30% and host executive customer engagements.

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.

The Chargebacks Analyst will conduct research on credit card chargebacks, minimize losses, review merchant requests, and support merchants in compliance with company policies. They will perform outreach, collaborate with cross-functional teams, and ensure accurate claim processing.

Lead design, implementation, and operation of Signifyd's GCP/Kubernetes cloud platform. Architect scalable, secure infrastructure, embed AI-driven automation, own SLOs and incident response, improve developer experience via CI/CD/GitOps tooling, mentor engineers, manage cloud capacity/FinOps, and build observability and security into the platform.

Supports recruiting by coordinating complex interviews, liaising with candidates and interviewers, resolving scheduling conflicts, collecting feedback, and sourcing candidates on LinkedIn. Also assists with HR reporting, analytics, compliance audits, digital file migration, employee experience initiatives, milestone management, and office administration. This is a 3–4 month remote contract role with a primary recruiting focus.

Lead and grow a machine learning engineering team responsible for fraud and risk models. Manage experimentation portfolios, balance research with delivery, evaluate model performance rigorously, and guide work through production release. Partner with Risk, platform, and infrastructure teams to set priorities, improve training and experimentation systems, communicate results, and make autonomous roadmap decisions. The role is a hands-on player-coach requiring technical depth, people leadership, strong communication, and comfort operating under uncertainty.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.