Top Analytics Jobs in Denver & Boulder, CO

Own the analytical function for a core business area, defining business questions, metrics, reports, analyses, experiments, and recommendations. Partner with product, growth, finance, and operations leaders to drive decisions and measurable outcomes. The role requires solving ambiguous problems, independently structuring and interpreting data, communicating insights persuasively, and executing initiatives end to end.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems and metrics, build reports, analyze data, design experiments, generate insights, influence leadership decisions, and drive initiatives through execution. The role requires strong first-principles thinking, independent data skills, persuasive communication, and ownership of ambiguous, high-impact problems.

Build scalable analytical products, data models, automation, and measurement solutions for the Individual Investors business. Apply advanced statistics, forecasting, machine learning, experimentation, optimization, and simulation to complex business problems. Use SQL, Python, cloud data warehouse practices, version control, testing, and documentation to create reliable, reusable capabilities. Partner with technical and business stakeholders, communicate insights, and improve analytical solutions through Agile delivery and engineering discipline.

Own the digital measurement ecosystem across websites, quote engines, customer portals, and account applications. Lead Google Tag Manager tracking frameworks, data-layer standards, Google Analytics 4 configuration, conversion events, attribution, funnel analysis, experimentation measurement, data-quality audits, and analytics governance. Partner with Marketing, Product, Engineering, and Data teams to translate customer behavior into actionable insights that improve growth, conversion, and customer experience.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Leads workforce analytics and planning, partnering with HR and business leaders to deliver predictive insights and strategic recommendations. Develops workforce planning frameworks, analytical standards, forecasting models, dashboards, and decision-support tools. Integrates workforce, financial, operational, and market data to identify talent risks, capacity needs, and organizational opportunities. Presents executive-ready data stories and coordinates actions across HR functions while coaching talent and improving analytics capabilities.

Lead and develop a team of partnership analysts; translate business questions into analytical frameworks, experiments, forecasts, and ROI models. Build dashboards and reporting infrastructure, analyze cohorts, funnels, retention, and partner performance, and own measurement frameworks for growth initiatives. Partner with Business Development, Engineering, Product, and Marketing to shape partnership strategy and communicate data-driven insights to technical and non-technical leaders.

Analyzes healthcare claims, pharmacy, laboratory, financial, utilization, and risk-adjustment data. Develops suspect, targeting, tracking, QA, and reporting systems for Medicaid, Marketplace, Medicare, and MMP interventions. Performs predictive modeling, statistical analysis, forecasting, anomaly detection, data validation, root-cause analysis, and risk-score tracking. Communicates analytical findings to technical and nontechnical stakeholders while supporting cross-functional projects and regulatory or business requests.

Leads internal data analysis projects, identifies business opportunities and risks, delivers strategic insights to leadership, manages junior analysts, develops dashboards, and improves data quality across company platforms. The role requires translating quantitative findings for nontechnical audiences and applying healthcare or pharmaceutical dataset experience to strategic decision-making.

Own client-facing analytics strategy and implementation across GA4, Adobe Analytics, tagging, measurement frameworks, dashboards, experimentation, and GEO measurement. Audit and QA analytics implementations, define KPIs, evaluate incrementality and causal relationships, present executive-ready insights, and collaborate with SEO, paid media, creative, data, engineering, and client teams. Build AI-assisted workflows, automations, scalable tools, and dashboards while mentoring teammates and managing multiple client workstreams.

Conduct quantitative research for traders by testing market hypotheses, building and maintaining documented signals, performing event studies and backtests, and validating results against leakage, survivorship bias, transaction costs, and regime dependence. Organize reproducible point-in-time datasets, communicate findings clearly, track signal performance, and use AI tools to accelerate research and coding while verifying outputs.

Own enterprise analytics audits, implementations, data quality, attribution, segmentation, SQL analysis, BI dashboards, experimentation measurement, and client reporting. Serve as the senior analytics partner for major accounts, present insights to executives, train client and internal teams, and help define analytics and experimentation services. The role requires hands-on Adobe expertise, autonomous prioritization, technical collaboration, and the ability to connect organic and AI search performance to pipeline and revenue.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Lead MyFitnessPal’s product analytics practice and team, advising the Chief Product Officer and senior leaders through actionable data insights. Define product metrics, oversee experimentation, analyze customer and marketing behavior, improve instrumentation and data infrastructure, and shape product strategy, roadmaps, engagement, and retention. This hands-on director role combines executive communication, team development, analytical leadership, and direct analysis across consumer iOS and Android applications.

Analyzes healthcare claims, provider, reimbursement, and financial data to support out-of-network dispute negotiations, arbitration, QPA accuracy, and IDR performance. Builds SQL-based reports, dashboards, data processes, metrics, and financial views; validates data quality; integrates internal and external sources; identifies trends and risks; and presents actionable recommendations to leadership and cross-functional stakeholders.

Lead a team of analysts delivering strategic analytics and business intelligence solutions. Establish data quality, governance, documentation, KPI, and reporting standards; oversee reconciliation and complex analyses; guide visualization and analytics practices; evaluate tools and automation opportunities; manage the team roadmap and performance metrics; and coach analysts while partnering with executives and stakeholders.

Own end-to-end product and customer analytics across financial product experiences. Define meaningful metrics, analyze complex datasets with SQL, connect customer behavior to business outcomes, and translate qualitative feedback into product insights. Partner with product, engineering, research, data science, and CX teams to influence roadmaps and senior leadership decisions. Use AI tools such as Cursor and Claude to accelerate analysis and automate workflows.

Lead real-world data analytics support for life sciences and technology clients in a post-sale capacity. Serve as the primary RWD subject matter expert, answering data questions, querying healthcare datasets with SQL or Python, guiding research protocols, delivering training, developing documentation, resolving issues, and identifying emerging client needs. Build trusted stakeholder relationships, collaborate with sales and delivery teams, lead data solutioning projects, and mentor junior analysts.

Lead end-to-end funnel analytics and optimization for SMB lending, identifying friction and bottlenecks, designing measurement frameworks, running experiments (A/B testing), and partnering with Risk, Operations, Product, Engineering, and AI teams to deploy automation and AI solutions that improve conversion, cycle time, and customer outcomes while managing risk.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead payments analytics by building scalable reporting, causal inference and ML models, designing experiments, partnering with product/engineering/finance to optimize payment processing, reconciliation, and settlement, and translating results into executive recommendations to drive global payments improvements.

The Predictive Analytics Consultant designs and delivers analytics solutions for lending operations, focusing on predictive modeling, data transformation, and stakeholder communication.

The role involves analyzing complex datasets for private equity transactions, providing insights for due diligence and performance, using various analytics tools and programming languages, and ensuring strategic decisions are informed.