Top Senior Data Science Jobs in Denver & Boulder, CO

Develop and operationalize advanced analytics for a large-scale government data platform. Responsibilities include statistical modeling, machine learning, predictive analytics, data exploration, model validation, experiment design, and integrating analytical capabilities into production workflows. The role collaborates with engineers, analysts, and government stakeholders to translate mission requirements into scalable solutions and communicate findings to technical and executive audiences.

Develop and operationalize statistical models, machine learning algorithms, and predictive analytics for a large-scale government data platform. Analyze complex datasets, identify patterns and anomalies, validate models, and integrate analytical capabilities into production workflows. Collaborate with engineers, developers, analysts, and government stakeholders to translate mission needs into scalable solutions and communicate findings to technical and senior audiences. The role requires an active TS/SCI clearance and is primarily onsite in Herndon, Virginia, or Westminster, Colorado.

Leads complex data science and AI initiatives, developing and deploying machine-learning solutions centered on LLMs, agentic workflows, and RAG systems. Translates stakeholder needs into technical roadmaps, mentors analysts and data scientists, manages project budgets and schedules, ensures quality, and designs reliable AI applications using embeddings, vector databases, retrieval systems, and AI workflow platforms.

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.

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.

Build production-grade marketing mix, forecasting, customer lifetime value, and incrementality models to guide budget allocation. Develop Databricks and Delta Lake ingestion pipelines, integrate GenAI agents for anomaly detection and reporting automation, and translate statistical insights into Tableau dashboards and executive strategy. The role also supports stakeholder training in Databricks SQL and requires at least one in-person interview.

Own the modeling and statistical lifecycle for enterprise AI initiatives. Build predictive models for anomaly detection, workflow forecasting, and risk mitigation; analyze and wrangle large-scale financial data; validate accuracy and bias; ensure regulatory compliance; create training datasets; establish model performance KPIs; and communicate quantitative findings to business and technology stakeholders.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

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 and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

The Data Scientist designs production machine learning, predictive, optimization, forecasting, ETL, and cloud data pipeline solutions for utility infrastructure operations. Responsibilities include analyzing complex operational and financial data, validating models against project outcomes, creating dashboards, communicating insights to stakeholders, establishing model governance, and supporting data-driven decisions across construction, procurement, finance, and engineering teams.

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.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

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.

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.

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, 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 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.