Top Senior Data Science Jobs in Denver & Boulder, CO

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

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.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Conduct research and analysis on structured and unstructured data, execute complex database queries, integrate diverse data sources, manage enterprise data environments, and derive actionable insights for national security missions. The role collaborates with multidisciplinary teams and communicates analytical findings to government stakeholders while supporting U.S. Army space and missile defense efforts.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Build and operate production calibration and image-processing pipelines for multispectral infrared satellite imagery. Translate calibration algorithms into reliable software with testing, monitoring, alerting, diagnostics, CI/CD, and observability. Analyze pre-launch and on-orbit data, validate calibration coefficients, manage uncertainty budgets, and support calibration campaigns for FireSat and SBEM missions. Collaborate with scientists, instrument engineers, and product teams to ensure radiometric integrity and scientific quality of data products.

Develop and productionize algorithms that transform multispectral infrared satellite imagery into derived geospatial data products. Build scalable Python-based processing pipelines, validate scientific quality, document product designs, and integrate operational services for customer-facing applications. Collaborate with scientists, software and instrument engineers, product owners, and customers across wildfire, weather, and environmental monitoring missions. The hybrid role requires three days weekly onsite in Denver, Colorado.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

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.

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.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.