Top Data Science Jobs in Las Vegas, NV

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive decisions and outcomes with cross-functional stakeholders. The role requires strong SQL or Python skills, independent data work, analytical problem-solving, and leadership-level influence.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent data work, strong analytical thinking, clear communication, and ownership of ambiguous problems that deliver measurable business impact.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

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.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Develop and maintain reproducible bioinformatics pipelines and multi-omics data products. Perform statistical modeling, ML, and integration of genomic, transcriptomic, spatial, proteomic, and clinical datasets. Build researcher-facing dashboards, notebooks, and APIs; ensure FAIR, containerized, version-controlled analyses; collaborate with scientists, engineers, and government stakeholders to enable reusable, secure, analysis-ready data workflows.

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

Designs AI risk stratification and evaluation methodologies for Veterans Health Administration systems, assessing performance, fairness, value, and post-deployment monitoring. Provides technical guidance on machine learning, healthcare data models, cohorts, synthetic data, and pilot evaluations. Develops risk management materials and supports governance decisions through quantitative analysis. Requires extensive applied data science experience, advanced education, and expertise in healthcare-focused machine learning.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

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.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Design, train, validate, and deploy traditional ML models and feature pipelines within Microsoft Fabric/Databricks. Partner with Data Engineers to optimize Medallion layers for model training and inference, implement MLOps (model registry, monitoring, retraining), perform EDA on large enterprise datasets, translate analytics to business insights, and document algorithm governance and fairness.