Top Tech Jobs & Startup Jobs in Seattle, WA

Lead the data science strategy and teams developing AI-powered compliance products for KYC, AML, identity verification, watchlist screening, risk modeling, and fraud prevention. Own the full model lifecycle, establish evaluation and monitoring practices, ensure explainability and governance, and partner with Product, Engineering, Risk, Operations, Legal, customers, and executives to deliver scalable, production-ready systems.

Build and lead the identity graph platform powering real-time fraud prevention. Responsibilities include defining graph schemas and ontologies, developing entity-resolution systems, designing batch and streaming pipelines, building low-latency APIs and serving systems, supporting ML and graph analytics, improving performance and reliability, leading architecture, mentoring engineers, and embedding privacy and security into production systems.

Serves as the technical client contact throughout sales and implementation, matching customer needs to identity and fraud solutions. Presents products, prepares statements of work and pricing, leads stakeholder meetings, coordinates cross-functional resources, supports enterprise engagements, and translates complex analytics into recommendations. Requires client-facing experience, fraud or credit risk analysis knowledge, business process re-engineering experience, strong project leadership, communication, presentation, prioritization, and collaboration skills.

Own full-cycle recruiting for go-to-market roles across sales, marketing, customer success, and revenue operations. Develop data-driven sourcing strategies, advise hiring managers, manage recruiting pipelines in Ashby, and deliver strong candidate experiences. Contribute to employer branding, process improvement, DEI, training, and talent planning while maintaining a SaaS sales and marketing network and mentoring junior recruiters.

Own the end-to-end data science lifecycle for workforce identity and hiring fraud detection. Analyze identity, device, behavioral, resume, and application data; develop fraud rules, machine learning models, and anomaly detection systems; and support deployment and monitoring in RiskOS. Collaborate on GenAI resume verification and explanation tools, evaluate LLM components, partner with engineering on productionization, and communicate model performance and customer impact to product, GTM, and consulting stakeholders.

Develop and deploy deep learning models for fraud detection, risk management, and identity verification. Work across diverse data modalities, including tabular data, text, point clouds, and images, throughout the machine learning lifecycle. Responsibilities include data exploration, feature engineering, model training, evaluation, deployment, monitoring, and communicating results. Collaborate cross-functionally, make technical decisions independently, and apply emerging AI and machine learning techniques to production challenges.

Own centralized Product Operations systems, reporting, analytics, and process governance. Build dashboards and metrics, improve data quality, standardize planning and prioritization workflows, facilitate cross-functional handoffs, and connect product strategy to roadmap execution. Support planning cycles, business reviews, and leadership reporting while enabling embedded product teams through documentation, training, tooling, and repeatable processes.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

Own the zero-to-one development and commercialization of Socure’s proactive identity notification products. Define the roadmap, event contracts, data-source strategy, KPIs, architecture, pricing, packaging, beta, and general availability launch. Evaluate data sources, measure coverage and precision, guide platform engineering decisions, recruit design partners, and coordinate cross-functional delivery. The role combines technical product management, data analysis, customer research, vendor and licensing judgment, and go-to-market ownership.

Leads data science for AML watchlist screening, developing multilingual NLP, entity resolution, information extraction, matching, risk-scoring, and graph-based detection systems. Builds data quality pipelines, evaluation frameworks, threshold tuning, backtesting, and production ML capabilities. Partners with Product and Engineering to deploy research at scale, leads technical initiatives, mentors peers, and translates model performance into compliance and business outcomes.