Top Data Science Jobs in Denver & Boulder, CO

Build predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

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.

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.

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.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Leads development and deployment of machine learning and graph analytics solutions to detect identity theft, account takeover, synthetic fraud, scams, disputes, and AML risks. Partners with business and technology teams on graph strategy, data integration, model governance, and production deployment. Develops reusable algorithms, communicates technical insights to executives, manages project risks, and mentors junior data scientists.

Build and deploy machine learning, NLP, and LLM-powered solutions at scale. Improve production model performance, reliability, and accuracy; develop datasets, metrics, tools, and experimentation frameworks; and use data to influence product strategy. Partner with Product, Engineering, and business stakeholders to drive AI innovation, while serving as a technical thought leader and mentoring other data scientists.

Own the design and build of a data-quality and assurance function for a large-scale pricing intelligence data lake. Responsibilities include architecting observability, defining quality standards, building anomaly detection and health scoring, instrumenting pipelines, answering customer data-quality questions, and tracing issues to root causes across collection, processing, and source changes.

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.

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.

Build and maintain predictive models throughout their lifecycle, identify data patterns and business insights, develop analytic procedures and automation, perform complex ad hoc analyses, monitor model recalibration, and review model scoring and data transformation quality controls.

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.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

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.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

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.

Designs, validates, and operationalizes predictive, machine learning, and AI models using large structured and unstructured datasets. Performs statistical analysis, develops visualizations and dashboards, presents findings to stakeholders, and integrates solutions across teams. Provides clinical-program analytics, evaluates data science tools, mentors junior scientists, leads concurrent projects, and prepares end-to-end model pipelines for business use.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

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 research projects that develop and improve data science methodologies and algorithms. Analyze existing solutions, build and test data extraction and transformation architectures, and collaborate with Technology and Product Management on deployment. Investigate production and client data issues, provide data analysis support, maintain SQL, Python, and R code, document processes, and present research findings to internal clients.