Top Remote Data Science Jobs in Kansas City, MO

Build and productionize AI/ML solutions on a modern AWS federal data platform: implement RAG/vector stores, develop and validate models and pipelines, automate via Airflow/Glue/Spark, integrate with ETL metadata, ensure security/compliance, write tests and documentation, and support agile delivery.

The Data Scientist role involves designing and deploying machine learning systems, prototyping AI agents, collaborating with product teams, and addressing business problems. Responsibilities include model building, data analysis, client collaboration, and contributing to AI tooling.

As a Staff Data Scientist, you will develop data-driven fraud prevention solutions using Generative AI, design scalable ML pipelines, and mentor junior data scientists while collaborating across teams to improve analytics and decision-making.

Lead end-to-end delivery of strategic data science and AI projects: define business problems with stakeholders, prepare large datasets, build and deploy predictive, prescriptive, and generative AI models (including LLMs), implement prompt engineering and fine-tuning, ensure production-grade code and ML lifecycle practices, present insights to senior stakeholders, and promote responsible AI and adoption across teams.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Lead embedded data science for a core business area (growth, product, AI, or partnerships). Define problems, build metrics and analyses, run experiments, surface insights, and drive actionable recommendations end-to-end to improve key business metrics.

Embed on a business unit to define problems, build metrics/reports/experiments, analyze data, and drive decisions end-to-end across growth, product, AI, or partnerships.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.

Lead development, validation, deployment, and quality control of advanced analytics and ML solutions for fraud detection, waste and abuse, investigative analysis, and program integrity. Provide technical leadership, translate stakeholder requirements, mentor team members, and ensure models are explainable, defensible, and production-ready.

The Senior Data Scientist will lead analytics efforts, develop data models, and improve data pipelines to support Thyme Care's mission in oncology care, while collaborating with stakeholders to enhance performance metrics and reporting.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.

The Lead Data Scientist will build, deploy, and improve machine learning models for a rental marketplace, managing projects from inception to launch and working collaboratively with cross-functional teams.

The Data Scientist collaborates with stakeholders to develop machine learning applications, analyze data, and enhance team operations, improving processes with data-driven insights.

The role involves designing, building, and deploying machine learning models, collaborating with teams to address business problems, and mentoring other data scientists.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Design and implement advanced ML models for real-time forecasting and decision support, ensure data provenance and lineage, integrate federated geospatial and multi-format data, produce ROM and quarterly cost/resource reports, and apply Agile sprint practices to deliver mission-aligned analytics for DoD/IC customers.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

The Data Scientist II will oversee data management, design study protocols, train staff, lead report development, and manage client activities using project frameworks.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead methodology and modeling for causal inference, experimentation, and predictive analytics. Design and evaluate experiments, build production-ready models and evaluation pipelines, collaborate with Product/Economics/Research/Engineering, communicate findings to varied audiences, and document institutionalized analytical practices.

Lead Revenue Analytics by developing data-driven strategies to maximize revenue, own and improve data pipelines, run advanced experiments and causal analyses, lead cross-functional initiatives, mentor junior analysts, and embed AI tools into workflows to scale impact.