Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

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.

Serves as the primary technical contact across sales cycles and implementations for identity verification products. Responsibilities include presenting solutions, designing proof-of-concepts, developing SOWs and pricing, translating technical concepts, coordinating stakeholders, advising clients, gathering product feedback, supporting sales engagements, contributing to thought leadership, and mentoring junior consultants. The role requires enterprise software experience, SaaS sales knowledge, strong technical aptitude, statistical communication skills, and up to 30% travel.

Own Socure’s developer marketing strategy for API and platform products, including positioning, technical messaging, developer campaigns, content, enablement, and community engagement. Partner with Product, Engineering, Sales, DevRel, Growth, and RevOps to drive developer signups, API activation, production conversion, and technical pipeline. Translate AI-driven identity and fraud capabilities into credible narratives, improve developer experiences, and measure funnel performance from first touch through adoption.

Own centralized Product Operations systems, tools, reporting, analytics, and processes. Administer product roadmapping and engineering work-tracking platforms, maintain integrations, build dashboards, define metrics, improve data quality, and standardize planning and prioritization workflows. Facilitate planning cadences, cross-functional handoffs, and strategy-to-execution alignment while enabling product managers and embedded Product Operations partners through documentation, training, and process improvements.

Own AML compliance frameworks, taxonomies, and regulatory intelligence supporting Socure’s platform. Partner with Engineering, Data Science, Product, Sales, and Customer Success to translate OFAC, FATF, FinCEN, and global requirements into product strategy, customer guidance, and scalable advisory services. Support enterprise compliance due diligence, audits, regulatory inquiries, and customer program design while developing automation to reduce operational burden.

Analyze large customer datasets, evaluate machine-learning models, develop risk and fraud insights, and communicate data-driven recommendations to technical and nontechnical audiences. The role partners with sales and account teams throughout pre- and post-sales, creates automated analytical workflows, supports product optimization and upsell opportunities, and may build exploratory ML models. It also contributes thought leadership on identity verification, fraud prevention, model performance, and customer risk management.

Design, develop, deploy, and monitor advanced deep learning models for fraud detection, risk management, and identity verification. Lead machine learning initiatives, research novel algorithms, manage end-to-end model lifecycles, collaborate with Product, Engineering, and Risk teams, and communicate insights to technical and executive stakeholders. Mentor data scientists and drive high-impact delivery, data quality, experimentation, and continuous improvement.

Own the strategy and roadmap for Socure’s Watchlist Screening and Monitoring product. Drive improvements in sanctions and AML screening accuracy, data coverage, scalability, reliability, and machine learning capabilities. Partner with Engineering, Data Science, Compliance Operations, Sales, Customer Success, and go-to-market teams. Analyze product metrics using SQL, translate regulatory and customer needs into priorities, support enterprise customers, and represent the product externally.

Drive organization-wide adoption of AI development standards, tools, and workflows across engineering. Build feedback loops, enablement content, prompt libraries, playbooks, training assets, and adoption dashboards. Partner with engineering leaders to improve AI practices, support onboarding and office hours, measure productivity and quality impact, and communicate progress through technical narratives and success stories.

Develop machine learning, statistical, graph-based, and entity-resolution algorithms for large-scale identity verification and deceased monitoring. Build Spark/PySpark and AWS data pipelines, engineer features, analyze data quality, conduct experiments, and support model evaluation. Write Python/R and SQL for extraction and validation, investigate compliance data, create dashboards, and communicate findings to cross-functional teams.