Top Data Science Jobs in Denver & Boulder, CO

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead data science initiatives across core business domains: collaborate with stakeholders and engineers, analyze large datasets, design experiments, build and productionize statistical and ML models, harden pipelines, monitor performance, and mentor teams to drive data-informed decisions.

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.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Analyze microbiome and longitudinal clinical data, develop biomarkers and microbiome metrics using statistical and machine learning methods, and create visualizations, reports, protocols, and study reports. The role supports research, product development, scientific publications, proposals, and clinical studies while collaborating independently with microbiome experts in a remote-first startup environment.

End-to-end data scientist who analyzes messy SEO/AEO data, designs and runs experiments, implements fixes using internal tooling, ships improvements, and communicates decision-ready reports to clients while improving tools and processes.

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.

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.

Develop and evaluate Python-based machine learning models using time-series telemetry and operational data. Responsibilities include data exploration, feature engineering, NLP, probabilistic and physics-informed modeling, anomaly detection, forecasting, validation, documentation, and production deployment. Collaborate with engineering, visualization, and program teams to integrate models into dashboards and decision-support tools. The role supports DoD customers and requires U.S. citizenship plus eligibility for a security clearance.

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.

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Develops end-to-end data science and analytics solutions for DoD mission environments. Responsibilities include ingesting and transforming structured and unstructured data, creating ETL pipelines, querying SQL databases, performing statistical and predictive analysis, building Power BI dashboards, integrating APIs and enterprise systems, improving data quality, automating workflows, and communicating insights to stakeholders. The role also supports data architecture, testing, documentation, and compliance with cybersecurity and DoD data-handling requirements.

Work as a healthcare data scientist/consultant delivering statistical analysis, predictive modeling, BI reporting, and data visualization for clients. Independently manage projects and client relationships, using R, SAS, SPSS, Cognos, and BusinessObjects. Report to the Senior Consultant.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Assists with analyzing complex datasets, developing predictive models for insurance underwriting, pricing, claims, and retention, and building data pipelines and automated reporting. Collaborates with actuarial, underwriting, claims, product, and data engineering teams to solve business problems. Communicates insights through visualization and data storytelling, monitors model performance and data quality, and explores new data sources and modeling techniques while supporting insurance model governance.

Develop mathematical, statistical, and machine learning models to solve complex problems across multiple domains. Analyze datasets, automate tasks, communicate analytical results, and align solutions with customer and management objectives. The role supports defense and space-related programs, requires collaboration with government and contractor teams, and is performed onsite in Colorado Springs.

Analyze, clean, and validate complex healthcare data; develop statistical, machine learning, and AI models; implement agentic workflows, RAG solutions, and model fine-tuning; deploy and monitor models in production; collaborate with technical and business teams; conduct research; and document and communicate findings.

Design and build thermal performance analytics, Python-based engineering models, scalable data flows, and Edge-native applications for power plant asset performance management. Integrate historian, cloud, Edge, and industrial systems; develop automation, diagnostics, KPI validation, and root-cause workflows; support customer deployments; and lead architecture discussions while mentoring engineering teams.

Develop predictive models, algorithms, quantitative analyses, and data visualizations from structured and unstructured data. Explore, clean, analyze, and interpret complex datasets using statistical programming, machine learning, NLP, and text-mining techniques. Collaborate with clients to identify data-driven solutions, lead solution development for complex programs, guide teammates, and provide insights supporting informed decisions.