Top Remote Data Science Jobs in Salt Lake City, UT

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.

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 high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.

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.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

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.

Develop and deploy production-grade machine learning and AI solutions for fleet analytics, logistics optimization, forecasting, anomaly detection, predictive maintenance, and operational decision-making. Build batch and real-time data pipelines, MLOps workflows, streaming analytics, LLM and RAG systems, analytical data models, and stakeholder dashboards using cloud platforms. Collaborate cross-functionally to translate business challenges into scalable data science solutions while supporting model governance, monitoring, experimentation, and documentation.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive decisions and outcomes with cross-functional stakeholders. The role requires strong SQL or Python skills, independent data work, analytical problem-solving, and leadership-level influence.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent data work, strong analytical thinking, clear communication, and ownership of ambiguous problems that deliver measurable business impact.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop and enable clinical data assets for analytics and AI/ML use cases. Responsibilities include designing clinical data models, taxonomies, feature stores, documentation, quality frameworks, dashboards, queries, and reusable datasets. Partner with clinical, business, engineering, and analytics stakeholders; support data governance and HIPAA compliance; translate clinical concepts into analytical frameworks; and train data consumers. Requires expertise in healthcare datasets, coding systems, SQL, Python, cloud platforms, and clinical informatics.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.