Top Remote Data Science Jobs in Boise, ID

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.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

Conduct complex actuarial and predictive analyses supporting insurance product development, pricing, rating, reserving, financial analysis, and process improvements. Develop large-scale predictive models, multivariate analyses, SQL queries, tools, workflows, and business processes. Collaborate with actuaries, product managers, and business teams, communicate insights, improve efficiency, and mentor less experienced staff.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Own the data science strategy and experimentation roadmap for ranking and relevance. Partner with product, engineering, ML, and commercial teams to improve marketplace outcomes through ranking models, experimentation, observational analysis, KPI frameworks, and commercial impact measurement. Balance advertiser, user, and platform value; guide launch and investment decisions; communicate findings to stakeholders; mentor scientists; and develop reusable analytical methods.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Develop and maintain predictive models for LTV, churn, retention, segmentation, personalization, and user acquisition decisions. Design experiments and causal analyses, apply LLMs to unstructured data, build AI-powered analytics tools, and productionize models with data engineering partners. Establish modeling standards, communicate insights to stakeholders, and mentor analysts and data scientists.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

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.

Leads fraud detection, risk analysis, loss mitigation, and fraud strategy development. Builds analytical rules, predictive algorithms, monitoring dashboards, and automated controls using SQL, Python, Tableau, and AI tools. Partners with product, engineering, modeling, and operations teams to define requirements, validate strategies, optimize fraud KPIs, and manage deployments. The role also involves experimental design, governance, onboarding fraud typologies, and mentoring junior team members.