Top Data Science Jobs in Denver & Boulder, CO

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

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.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

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.

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.

Develop and evaluate machine learning models for fraud detection across account opening, account takeover, and identity risk. Responsibilities include exploring large datasets, identifying fraud patterns, engineering features, testing models, writing production-quality Python code, supporting model monitoring, and communicating analytical findings. The role also applies data privacy, explainability, validation, documentation, and governance standards while collaborating with senior data scientists and engineering teams.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

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.

Develops data science and machine learning solutions for mission and business needs. Responsibilities include preparing structured and unstructured data, creating features and models, evaluating performance, supporting validation, producing visualizations and reports, assessing data quality, documenting work, and collaborating with stakeholders to refine requirements and inform decisions. The role is fully onsite in Chantilly, Virginia, or Aurora, Colorado.

Supports data analysis, predictive model development and monitoring, partner oversight, data quality investigations, and analytical solution documentation. Uses Python, SQL, cloud and visualization tools to analyze complex datasets, automate monitoring and validation workflows, identify root causes, and communicate insights to technical and business stakeholders. Collaborates with risk, compliance, technology, operations, vendors, and external partners in a regulated financial services environment.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Leads a team of data scientists delivering predictive modeling, machine learning, simulation, optimization, and automated analytics solutions. Partners with business and technology leaders to scope projects, drive insights, manage portfolios, oversee model governance, ensure regulatory compliance, and communicate results to executives. Responsibilities include recruiting, coaching, performance management, resource planning, innovation, and advancing enterprise data science capabilities.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Designs AI solutions for fund administration workflows, selecting models, prompts, retrieval, agents, rules, or human review. Builds evaluation datasets and metrics, runs offline and pilot evaluations, analyzes failure patterns, measures accuracy, efficiency, and cost savings, and packages successful approaches into reusable patterns.

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.

Develop data products and analytical solutions supporting USSPACECOM. Responsibilities include building pipelines in Foundry and Databricks, applying SQL, Python, PySpark, and TypeScript, designing ontologies and data models, creating statistical and decision-support tools, validating data quality, developing dashboards and workflows, and briefing findings to technical and senior non-technical stakeholders.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

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.

Analyzes and models data to generate insights that improve client campaign performance. Responsibilities include data extraction and transformation, statistical analysis, predictive modeling, data visualization, client presentations, project delivery, stakeholder collaboration, and development of standardized analytical procedures. The role also supports marketing research, value-added analyses, AI-driven solutions, sales materials, and code collaboration using version-control tools.

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.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.