Top Data Science Jobs in Denver & Boulder, CO

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

Embedded senior product data scientist partnering with discovery and conversion squads to run high-throughput experiments, define KPIs and dashboards, perform causal and behavioral analyses, build self-service analytics and pipelines, mentor peers, and drive analytics strategy to improve acquisition, conversion, and subscription performance.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Work with clients and engineers to audit marketing datasets, build analytics pipelines, and apply predictive models (segmentation, attribution, propensity) using SQL, Python, cloud big-data tools, and ML frameworks. Support implementations, develop analytic applications, and communicate findings to drive marketing outcomes.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

The Data Scientist 1 role involves cleaning and analyzing complex datasets, developing statistical models, and communicating insights through data visualizations. Responsibilities include collaborating with teams, maintaining data quality, and keeping up with advancements in the field.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Build and deploy predictive and time-series models using HR and clinical data (Oracle HCM, GCP) to forecast staffing, identify flight-risk employees, analyze pay/burnout relationships, and create interactive dashboards and analyses to inform HR strategy and operational workforce decisions.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

The Data Scientist II will drive insights, develop models, and analyze product experiments to inform business decisions and improve member experiences in healthcare.

Perform quantitative real estate and urban economic analysis, applying econometric and spatial methods to synthesize property, macroeconomic, and geospatial data. Build and maintain automated data pipelines, ensure data integrity, document dataflows, and prototype ML/AI methods. Collaborate with research, GIS, and engineering teams to produce reproducible insights and analytical products for stakeholders.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

The Senior Applied AI Data Scientist will build AI workflows, prototype applications, and enhance decision-support systems while collaborating with various teams to operationalize AI solutions.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Design, implement, and maintain marketing inference models (e.g., MMM and attribution). Analyze large datasets and A/B/uplift experiments with rigorous statistics, build predictive and causal models, partner with marketing to generate testable ideas, and communicate insights to drive marketing decisions.

Analyze large marketing datasets, build predictive models for segmentation, pricing, and demand forecasting, support targeted campaigns and retention, partner with product and marketing, apply statistical and ML methods, ensure data quality with engineering, and communicate insights to stakeholders to drive conversion and retention improvements.