Top Remote Machine Learning Jobs in Portland, OR

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

As a Credit Risk Senior Analyst at Marqeta, you'll lead underwriting strategies, refine credit policies, conduct risk analyses, and present findings to senior management.

Lead payments analytics by building scalable reporting, causal inference and ML models, designing experiments, partnering with product/engineering/finance to optimize payment processing, reconciliation, and settlement, and translating results into executive recommendations to drive global payments improvements.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Design and implement data integration and data warehouse solutions using big data technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and machine learning knowledge, and implement real-time streaming and aggregation patterns.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Analyze large federal and state economic datasets, automate data collection and scraping, and rapidly publish clear, data-driven reporting on market-moving indicators. Collaborate with reporters and visual journalists, file public-records requests, and use emerging technologies including AI to enhance real-time coverage.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Lead strategy and execution to prevent, detect, and disrupt digital fraud, scams, and money mule activity. Build and mentor data science and analytics teams, develop ML predictive models, partner with product, cyber, vendors, and risk teams, and drive prevention-first controls, compliance, and operational efficiencies across payments, onboarding, and authentication channels.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Design and scale fraud-mitigation strategies for buyer, seller, and account fraud across Stripe products. Monitor portfolios, identify and predict risky behavior, propose controls, automate workflows, collaborate with Product/Engineering/Data Science, and educate users to minimize losses while enabling growth.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Analyze historical demand, occupancy, transactions and competitor pricing to optimize daily, monthly, and event pricing. Build dashboards, deliver revenue insights to leadership, and collaborate with Operations and Marketing to implement pricing and promotional strategies while maintaining data integrity.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.