Top Data Science Jobs in Seattle, WA

Develop and deploy machine learning models and data pipelines for automated decision-making. Responsibilities include collecting and transforming data, exploratory analysis, predictive modeling, production deployment, model serving infrastructure, monitoring drift and performance, and communicating insights to stakeholders. The role requires strong Python and SQL skills, experience with cloud platforms and MLOps tooling, and collaboration across global teams.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Leads growth marketing analytics and measurement strategy, manages and develops a team of approximately three data scientists, and drives advanced statistical and machine learning initiatives. Oversees cross-channel performance analysis, causal measurement, experimentation, automation, data pipelines, scalable dashboards, and recurring reporting. Influences VP and SVP stakeholders, translates ambiguous business problems into analytical programs, establishes technical standards, and communicates insights to technical and executive audiences.

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 statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Own data science for payment performance, optimization, timing, retry strategies, monitoring, experimentation, and fraud detection. Build payment-health scorecards, anomaly monitoring, payment-risk models, and production ML systems using Python, SQL, PySpark, Databricks, and MLOps tooling. Partner with Engineering, Product, and Risk to shape payment strategy and roadmap while applying causal inference, feature engineering, and production model monitoring.

Lead commercial data science for Headway's commercial business: own roadmap, run proactive strategic analyses for executive accounts, monitor key metrics, enable self-service analytics, mentor data scientists, and translate technical findings into actionable recommendations tied to measurable outcomes.

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.

Staff and Principal Product Data Scientists will use advanced analytics, experimentation, causal inference, machine learning, and large-scale data technologies to guide Slack product strategy. They will define success metrics, build self-service reporting and data models, evaluate product adoption drivers, identify growth opportunities, and help develop Slack’s agentic data delivery platform. The role partners closely with Product, Design, Engineering, Product Research, and go-to-market teams while communicating insights to executives and cross-functional stakeholders.

Lead end-to-end data science for Cash App Lending: define measurement frameworks, run experiments, evaluate model and policy performance, use AI tools to accelerate analysis, partner with product, engineering, and risk, and mentor junior data scientists to drive product and risk decisions.

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.

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

The Senior Data Scientist will analyze product, customer, and risk data to improve Support and Risk Operations. Responsibilities include defining metrics, forecasting demand, modeling staffing and queue health, evaluating experiments and policies, monitoring risk models for bias and drift, and communicating decisions to product, engineering, and risk stakeholders. The role also leads technical standards, develops reusable analytical tooling, mentors data scientists, and uses AI tools and agent workflows to improve analytical quality and efficiency.

Analyze product, customer, and risk data to guide Support and Risk Operations decisions. Define metrics, forecasting and staffing models, evaluate experiments and risk policies, monitor bias and model performance, and build scalable measurement frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, risk, and business stakeholders, communicate insights, provide technical leadership, mentor data scientists, and contribute to hiring.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

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 development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Use statistical, algorithmic, and visualization techniques to model business problems, derive actionable insights from member data, and inform product and service decisions. Collaborate with end-users, build analytic capabilities, communicate data-driven recommendations, and promote advanced analytics adoption across the organization.

Partners with engineering, product, and business teams to frame problems, perform exploratory analysis, build statistical and machine learning models, write production code, run experiments, analyze data, communicate findings, and support launch decisions.

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.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Develop and deploy machine learning models across the full lifecycle, including experimentation, MLOps, monitoring, explainability, and governance. Build scalable model-serving and feature pipelines, contribute to Spark-based data engineering in Databricks and Snowflake, and support orchestration, metadata, security, and compliance practices. Collaborate with platform teams on environment configuration and troubleshooting within HIPAA-governed and FedRAMP-compliant healthcare analytics environments.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.