Top Data Science Jobs in Denver & Boulder, CO

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.

Develop and deploy LLM and machine learning models for Airbnb Community Support. Responsibilities include automating LLM evaluation, curating synthetic datasets, analyzing customer issues, identifying model failure modes, building personalization models, exploring data, prototyping solutions, and communicating insights to cross-functional stakeholders. The role focuses on scalable, production-ready AI products and operational optimization.

Evaluate migration technologies, build quantitative models for migration risk and data quality, coordinate customer migration projects, develop data pipelines and analytical frameworks, implement Python and SQL validation workflows, assess cloud environments, and create dashboards and reporting for product, sales, and operations teams.

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.

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.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior 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.

Supports migration of reporting assets from SAP BusinessObjects to Qlik by rebuilding, testing, validating, documenting, and troubleshooting reports. Responsibilities include creating dashboards, writing SQL queries, updating Qlik scripts and expressions, tracking migration changes, migrating scheduled jobs, and supporting user acceptance testing and training documentation under senior developer guidance.

Analyzes structured and unstructured datasets supporting U.S. Navy electronic warfare systems. Develops statistical, machine learning, NLP, data modeling, visualization, and reporting solutions to improve system readiness, reliability, maintainability, operational availability, and lifecycle performance. Integrates logistics, engineering, maintenance, and operational data; identifies trends and risks; and presents data-driven findings to technical and program stakeholders.

Develops analytical models and applications for U.S. Navy electronic warfare and sustainment systems. Applies data mining, statistical analysis, NLP, machine learning, and data modeling to structured and unstructured data. Integrates logistics, engineering, maintenance, reliability, and operational data to assess readiness, performance, risks, and availability. Creates visualizations, dashboards, reports, and briefings, while collaborating with engineers, logisticians, operations researchers, and program personnel to support lifecycle management and data-driven decisions.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Data Scientist I will support Sportsbook and Casino commercial strategy through advanced analytics, machine learning, personalization, promotion engines, and lifetime value modeling. Responsibilities include analyzing player behavior, developing segmentation and LTV models, conducting experiments and causal analyses, collaborating with stakeholders and data teams, and delivering data-driven recommendations. The role requires strong Python, SQL, statistics, experimental design, and machine learning skills, with iGaming, customer lifetime value, or generative AI experience preferred.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Supports energy efficiency and load management programs by maintaining and enhancing predictive analytics models. Analyzes large customer, energy usage, and marketing datasets; develops and executes code; validates analytical outputs; and prepares technical reports. The intern applies statistical and computational methods, integrates analyses with IT systems, and communicates technical findings to business and client stakeholders. This is a 10-week, full-time summer internship with hybrid or remote work options within the United States.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Product Data Scientist responsible for product data modeling, product and revenue analytics, behavioral analysis, A/B experimentation, and product metric governance. The role partners with Product, Revenue, Engineering, and Analytics Engineering teams to deliver scalable insights and influence decisions. It also contributes to agentic analytics by evaluating AI-generated outputs and improving semantic and prompt layers.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.