Top Senior Data Science Jobs in Seattle, WA

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

As a Data Scientist at Opendoor, you'll enhance pricing strategies through statistical models, machine learning, experiments, and interdisciplinary collaboration to drive business outcomes.

Lead end-to-end data science for lending products: define metrics, run experiments, build measurement systems, evaluate model and policy performance, use AI tools to accelerate analysis, and mentor the team while partnering with product, engineering, and risk stakeholders to drive credit product decisions.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Lead product-focused data science and analytics work: define success metrics, design and analyze A/B tests, investigate product and customer performance, build anomaly detection frameworks, and translate insights into product and roadmap recommendations while partnering with cross-functional teams.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Build scalable, end-to-end machine learning and data science solutions for Walmart Marketplace. Own MLOps infrastructure, monitoring, training and inference pipelines, data feedback workflows, and cloud deployments. Apply SRE principles, lead engineering best practices, mentor junior machine learning engineers, provide architectural guidance, and collaborate with product and business stakeholders to deploy impactful models.

Provides technical leadership for production-scale data science and AI systems. Designs end-to-end ML platforms, autonomous and multi-agent systems, NLP, computer vision, forecasting, reinforcement learning, simulation, evaluation frameworks, and MLOps processes. Leads high-impact initiatives from problem framing through deployment, influences AI strategy, presents quantified insights to executives, establishes validation and safe-deployment standards, and mentors senior scientists and engineers.

Senior Data Scientist will design, validate, and deploy scalable ML models (recommendation systems, NLP), source and ensure data quality, assess model performance, produce visualizations, collaborate with stakeholders, and mentor junior team members to deliver data-driven solutions that optimize business outcomes.

Collects, explores, and analyzes large datasets to identify business opportunities and improve outcomes. Develops predictive models, machine-learning algorithms, testing frameworks, and data pipelines; evaluates data quality and model performance; and communicates actionable insights to stakeholders and clients. The role also manages data science projects, establishes reporting best practices, and collaborates with cross-functional teams to implement and monitor solutions.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Build models to predict infrastructure resource needs, analyze telemetry and usage to inform capacity planning, optimize compute provisioning and costs, collaborate with engineers and finance, and provide data-driven recommendations to improve infrastructure reliability and efficiency.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

As a Data Scientist in Marketing, you will enhance AI-driven marketing strategies, collaborate with teams to develop metrics, analyze marketing data, and recommend improvements for user engagement.

Build and optimize machine learning models, analyze structured and unstructured data, perform feature engineering and evaluation, and identify insights for business decisions. Create dashboards and data visualizations, communicate findings to technical and non-technical stakeholders, improve data quality, and maintain data integrity and confidentiality in regulated healthcare-related environments.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.