Top Remote Data Science Jobs in Spokane, WA

Develop and deploy ML-based fraud and risk models to detect financial crime and reduce losses. Design A/B tests, create risk signals and monitoring metrics, build tools and dashboards, provide technical leadership, and perform ad-hoc analyses in collaboration with Risk, Compliance, Product, and Marketing teams.

Lead data-science projects with customers to acquire and prepare data, develop and evaluate production-scale models, drive model best practices, contribute thought leadership and open-source solutions, and define business and solution strategy while collaborating across engineering teams.

Design, build, and productionize complex Bayesian and probabilistic modeling pipelines. Lead methodology and technical direction, collaborate with Product/Data/Engineering, mentor data scientists, communicate results to stakeholders, and apply hierarchical, state-space, time-series, and graphical modeling at scale with strong emphasis on uncertainty and model validation.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead research defining fabric's AI shopping roadmap: design and run quantitative studies, build models and benchmarks for AI search (AEO/GEO), run experiments, translate findings into customer-facing insights, and publish research to influence product and go-to-market strategy.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead advanced analytics and ML for partner-facing teams: build end-to-end predictive models (churn, lead scoring, health scores), marketing attribution and mix models, and maintain ML lifecycle standards. Develop agentic workflows and Claude Skills to accelerate work, mentor analytics team, and translate model outputs into actionable insights for Sales, Marketing, and CX.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

The Senior Data Scientist will design and deploy machine learning models, analyze complex datasets, develop analytical solutions, and mentor junior team members in a remote environment.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

Build and maintain analytics data models, dashboards, and reports. Use SQL (Postgres, Trino, Iceberg) and dbt to power financial metrics. Perform deep-dive analyses using SQL and Python (Jupyter, Pandas) to answer business questions, operationalize data, and improve reporting and automation.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

The Senior Data Scientist will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

Lead retention and churn efforts by building survival and lifecycle-state models, classifying churn reasons, creating personalized win-back and next-best-action interventions, optimizing offers/promos, designing uplift experiments, and owning production model lifecycle, monitoring, and MLOps across Product, CRM, Marketing, and Engineering.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

The Senior Data Scientist leads data-driven initiatives, optimizing analytics for business decisions, mentoring staff, and ensuring data quality across healthcare projects.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

Lead end-to-end data science and AI engagements: design, develop, deploy and evaluate models, mentor teams, partner with stakeholders, drive adoption, and apply Responsible AI to deliver measurable business impact.

Lead the intelligence layer atop the data lakehouse: design and run causal experimentation, build predictive and forecasting models, own model evaluation and monitoring, deploy and maintain models on Databricks/MLflow, set technical direction for data science, and partner closely with data engineers, AI engineers, and product. Use AI development tools daily.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

Lead development and deployment of machine learning models, perform EDA and data engineering on large datasets, train models including LLMs and unstructured-data solutions, build Power BI dashboards, automate processes, present findings to leadership, and mentor junior data scientists while working with SQL, Python, AWS/Azure, Snowflake, and Salesforce.