Top Data Science Jobs in Seattle, WA

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Lead advanced analytics, statistical modeling, machine learning, and generative AI initiatives for Microsoft’s sales, marketing, and support platforms. Translate business needs into measurable problems, develop robust models and reusable code, evaluate and optimize GenAI solutions, diagnose model issues, design experiments, and deliver statistically reliable insights. Collaborate with engineering, product, and data science teams to drive product improvements and AI transformation.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

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.

Evaluates AI-generated data science and quantitative reasoning outputs; develops prompts, datasets, reference solutions, and objective evaluation benchmarks. Identifies statistical errors, flawed methodologies, and unsupported conclusions while providing structured feedback to improve model accuracy. Responsibilities include dataset validation, statistical and quantitative assessment, quality assurance, technical documentation, and clear communication of analytical findings.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Own complex data science projects applying machine learning, generative AI, statistical methods, and natural language processing to legal knowledge management and regulatory intelligence. Build systems for retrieval, summarization, classification, requirements extraction, impact analysis, and decision support. Establish data foundations, evaluation metrics, human-review workflows, and responsible AI practices while partnering with legal, policy, research, engineering, and business stakeholders. Mentor practitioners and operationalize scalable, auditable solutions.

Lead ecosystem-wide measurement strategy for Microsoft AI products. Define metrics, experimentation frameworks, causal-inference analyses, and evaluation systems that guide product strategy, investment, and resource allocation. Partner with product, engineering, business, and executive leaders to identify opportunities, quantify impact, and align organizations. Provide technical leadership, mentorship, and analytical best practices across large-scale data science and experimentation initiatives.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Build prototypes, conduct experiments, analyze large-scale data, and advance attribution capabilities from early concepts through production. Collaborate with engineering, product, analysts, and account teams to improve measurement and marketing ROI. Responsibilities include owning measurement products, applying Bayesian and marketing models, writing production code, and communicating methodologies and findings to technical and nontechnical audiences.

Develop AI and machine learning solutions for fraud, anomaly detection, semantic search, entity resolution, and investigative analytics. Build and operationalize models across structured and unstructured data using AWS data lakehouse services, generative AI, NLP, graph analytics, and document intelligence. Evaluate model performance, explainability, monitoring, and risk scoring while supporting production integration, dashboards, and investigative workflows. Collaborate with technical teams, investigators, stakeholders, and federal clients in an Agile environment.

Build Copilot quality measurement and decision systems using customer, product, evaluation, and usage signals. Responsibilities include prioritization frameworks, taxonomies, representative evaluation portfolios, quality gates, scorecards, causal analysis, sampling, clustering, trend detection, and automated reporting pipelines. The role connects offline evaluations with online customer outcomes, identifies coverage gaps and failure patterns, supports product and model investment decisions, and communicates recommendations to technical, product, and executive stakeholders while maintaining privacy, provenance, reproducibility, and metric governance.

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.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Develops and deploys AI and LLM-driven features for Microsoft 365 Copilot, including model evaluation, metrics, experimentation, retrieval-augmented generation, prompting, and agentic system optimization. The role prototypes scalable AI applications using Azure AI Foundry and LangChain, diagnoses model issues, and supports improvements to deep learning, NLP, and multimodal systems.

Develops Copilot usage and quality metrics, product insights, instrumentation, and large-scale A/B experiments. Analyzes telemetry with statistical and machine-learning methods, builds data pipelines, evaluates feature performance, and communicates data-driven recommendations to leadership and cross-functional teams. Requires expertise in product analytics, experimentation, complex data, algorithms, and LLMs.

The Principal Data Scientist will analyze large datasets, develop metrics systems, dashboards, business models, and A/B tests to improve MSN content relevance, recommendation performance, user engagement, and business growth. The role partners with cross-functional stakeholders, identifies trends and root causes, evaluates product performance, recommends improvements, and communicates actionable insights to technical and non-technical audiences.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Leverage data to support Slack growth initiatives, identify customer behavior trends, forecast performance, and uncover growth opportunities. Partner with Growth, Data Engineering, Strategy, Go-To-Market, Product, Design, and Engineering teams. Build analytical frameworks, core data models, self-service tools, and reports, while communicating insights and recommendations to senior leadership.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Develop and productionize machine learning and AI solutions across document intelligence, multimodal systems, and foundation models. Responsibilities include dataset and pipeline development, model training and evaluation, GPU experimentation, reusable AI component design, production deployment partnership, monitoring, and communicating technical tradeoffs. The role requires solving open-ended problems in regulated financial services while emphasizing explainability, reproducibility, data governance, reliability, and measurable business impact.