Top Remote Data Science Jobs in Spokane, WA

Lead end-to-end data science implementations for manufacturing clients using Azure and Microsoft data technologies. Build and deploy predictive models, analyze structured and unstructured data, develop scalable architectures, and deliver actionable insights. Mentor junior team members, manage complex client projects, gather requirements, support proposals, and communicate technical findings to business stakeholders. The role also contributes to data strategy, automation, process improvement, and innovation while requiring approximately 10% travel.

Leads architecture and delivery of production-ready data science, AI, machine learning, forecasting, optimization, and analytics solutions on Azure. Partners with clients, translates business needs into scalable technical designs, guides modeling and deployment, and manages a Senior Data Scientist. Responsibilities include technical leadership, client engagement, model monitoring, stakeholder communication, solution estimation, mentoring, and delivery oversight across complex and ambiguous data environments.

Develop statistical models, experiments, causal inference methods, and scalable analytical workflows to generate business insights. Partner cross-functionally with Client Experience, Strategy, Marketing, Styling, Support, and Engineering teams to define metrics and improve products. Own analytical initiatives from framing through delivery, communicate executive-ready recommendations, mentor data scientists, and leverage LLMs and AI agents to accelerate analysis and workflows.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

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.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Own mobile marketing analytics frameworks, dashboards, cohort and campaign analysis, profitability modeling, audience strategy, and incrementality experiments. Analyze attribution, retention, LTV, CAC, ROAS, payback, creative performance, and marketplace dynamics. Partner with marketing, finance, and leadership to guide spend allocation and growth decisions while navigating mobile privacy and attribution challenges.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.

Lead the strategic direction and evolution of Netflix’s company-wide Experimentation Platform. Establish rigorous causal inference and experimentation standards, automate trust and validation practices, consolidate fragmented systems, shape platform product roadmaps, and collaborate with data science and engineering leaders. Translate experimentation needs into scalable tools, influence adoption across teams, mentor colleagues, and ensure reliable experiment allocation, measurement, analysis, and reporting.

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.

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.

Design, develop, and deploy supervised machine learning models for credit decisioning. Collaborate with business and technical stakeholders to create data sources, improve modeling methodology, operationalize models, and manage model evaluation and risk for production systems.

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Lead G2’s experimentation practice across products and digital marketplaces. Design experimentation standards, causal inference methods, metrics, guardrails, and analysis approaches; partner with product and engineering to implement experimentation platforms and instrumentation. Build models, design auctions, ship production systems, evaluate modern statistical methods, and address marketplace interference and low-traffic constraints. Mentor data scientists and evangelize rigorous data science practices across the organization.

Leads data science initiatives by analyzing complex datasets, developing and deploying machine learning and generative AI solutions, and applying RAG, fine-tuning, and agentic workflows. Improves healthcare outcomes, Star ratings, care gaps, payment integrity, and member experiences. Collaborates with technical and business teams, communicates analytical findings, documents methodologies, manages projects, and mentors data scientists.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

The Senior Data Scientist will analyze large-scale customer data, investigate behavioral questions, design and automate studies, and translate findings into actionable recommendations. The role includes maintaining analytics frameworks, documenting methodologies, writing white papers, and communicating insights across the company. Candidates should be independent, comfortable with ambiguity, and experienced with SQL and Redshift. Python or R, machine learning, Spark, A/B testing, and cross-functional experience are preferred.