Top Data Science Jobs in Houston, TX

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.

Design, build, and validate agentic AI systems and multi-agent workflows for financial use cases. Lead cross-functional teams to productionize models, create evaluation frameworks and gold-standard datasets, develop reusable agent patterns and prompts, integrate AI into services, and implement systematic testing to meet compliance and reliability standards.

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 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 customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

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 and deliver ML and AI projects supporting government services. Handle end-to-end analytics lifecycle: data extraction/cleaning, statistical modeling, clustering, crime data vectorization, and generative AI features including prompt engineering. Collaborate cross-functionally and communicate findings to technical and non-technical stakeholders.

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 a data science team to build scalable analytics and predictive models for pricing decisions. Partner with business and engineering to analyze large datasets, prioritize work, ensure model quality, and present data-driven optimization recommendations.

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.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.