Top Machine Learning Jobs in Portland, OR

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leads customer and marketing analytics strategy, managing a small analytics team and partnering with executives to improve acquisition, retention, engagement, conversion, and customer experience. Develops KPIs, scorecards, journey reporting, cohort analyses, campaign measurement, segmentation, and A/B testing programs. Oversees scalable analytics frameworks, data quality, governance, architecture, and integration across customer touchpoints. Applies advanced analytics and predictive modeling to deliver actionable insights and presents recommendations to senior leadership and board-level audiences.

Leads analytics and reporting for Microsoft’s Cloud Solutions Provider authorization ecosystem. Develops actionable insights, dashboards, data visualizations, and self-service reporting; improves data quality, governance, standardization, and efficiency. Partners with engineering, AI, operations, and compliance teams to automate workflows, identify risks, evaluate program outcomes, and recommend improvements. Presents findings to senior stakeholders and translates business needs into technical requirements and scalable analytical solutions.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

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 CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Own data quality across systems by designing validation rules, reconciliation processes, profiling workflows, and quality controls. Investigate discrepancies to identify root causes, apply statistical methods to detect anomalies, evaluate machine learning model outputs, and communicate findings to technical and non-technical stakeholders. Partner with engineering, BI, and business teams to establish data quality standards, metrics, controls, and reporting practices.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Leads the enterprise data strategy, builds and scales the Data & Analytics team, and oversees data warehouse modernization, migration, governance, KPI standardization, and data quality. Partners with executives and cross-functional teams to convert data into actionable insights, operational improvements, predictive analytics, and AI-enabled workflows. The role requires hands-on Python, SQL, cloud, machine learning, and ETL expertise, plus strong leadership and communication skills.

Leads enterprise data, analytics, business intelligence, and AI strategy across healthcare payer functions. Partners with executives to prioritize analytics initiatives, build reporting and predictive insight capabilities, establish data governance and accountability, and drive measurable business outcomes. Oversees cross-functional teams, enterprise application development, Azure architecture, quality automation, DevOps, and intelligent automation while engaging C-suite and board stakeholders.

Collects and analyzes performance data, identifies trends, evaluates program effectiveness, and develops reports and visualizations for stakeholders. The role standardizes datasets, improves data collection and analytical methods, and supports data-driven decisions. It requires statistical analysis, data manipulation, visualization, and familiarity with machine learning and predictive analytics. The position is contingent on contract award and requires the ability to obtain and maintain an active Secret Clearance.

Designs and supports scalable cloud data warehouses for CMS healthcare fraud, waste, and abuse analytics. Responsibilities include data modeling, healthcare interoperability, secure HIPAA-compliant governance, lineage, testing, CI/CD, automation, workflow scheduling, and technical architecture. The role integrates claims and healthcare domains using Snowflake and industry standards while collaborating with stakeholders, documenting solutions, and leading warehouse project delivery.

Analyzes complex healthcare data to identify trends, develop statistical and machine learning methodologies, and produce actionable insights. Builds dashboards and reports, supports product and business decisions, promotes data literacy, and collaborates with cross-functional teams. The role also ensures analytics practices comply with healthcare privacy, governance, security, and regulatory requirements, including HIPAA.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

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.

Supervise actuarial predictive modeling initiatives for insurance pricing, portfolio management, and planning. Collaborate with actuaries, data scientists, underwriters, and management to develop, validate, govern, and implement models. Establish assumptions, loss adjustments, usage guardrails, monitoring practices, and model documentation. Support regulatory filings, improve team workflows, contribute to strategic projects, and mentor staff in actuarial and technical skills.

Develop and maintain quantitative models for forecasting, pricing optimization, and risk assessment. Analyze mortgage business metrics to identify revenue, profitability, and operational improvement opportunities. Build performance measurement frameworks, dashboards, automated reports, and SQL-based analytics workflows. Use Python for statistical analysis, automation, and machine learning workflows. Validate and monitor model performance, recalibrating models as needed, while communicating actionable recommendations to business leaders.

Develop statistical, machine learning, and deep learning models to measure the impact of pharmaceutical HCP, DTC, and Medical Affairs campaigns. Partner with internal and external clients, provide analytical insights and methodological guidance, consult with sales and market research teams, and coach an offshore analytics team. The role requires applying causal inference, healthcare and pharmaceutical data expertise, and clear communication of findings to clients and executives.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Designs, builds, and maintains data pipelines, processing systems, APIs, ETL processes, and data integration infrastructure. Implements data quality and governance practices, supports data scientists and analysts, and works with data warehouses, data lakes, cloud technologies, and large datasets. The role also requires ongoing learning about data technologies and occasional driving.