Top Data Science Jobs in Houston, TX

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Develops advanced analytics and machine learning models using structured and unstructured data to generate actionable business insights. Responsibilities include defining analytics requirements, selecting modeling methodologies, integrating models into business processes, measuring performance, enhancing analytics solutions, creating visualizations, and communicating findings to stakeholders. The role requires collaboration with application developers and independent judgment in solving technical and business problems.

Develop and maintain predictive Customer Lifetime Value models and eCommerce forecasts using transaction and behavioral data. Build statistical and machine learning models for revenue, retention, purchasing behavior, and customer demand. Perform cohort analysis, segmentation, scenario modeling, and advanced analytics while partnering with Marketing, Finance, Product, Data Engineering, and Analytics teams. Explore AI and LLM applications, communicate insights to stakeholders, and support scalable analytical workflows.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Develops and deploys complex Generative AI and machine learning solutions across HP projects. Responsibilities include building and monitoring models, defining performance metrics, identifying business insights, collaborating with stakeholders, creating visualizations, ensuring scalable implementation, and leading data science project teams. The role also provides technical guidance, training, and mentoring while working with large language models, multimodal models, RAG, vector databases, and cloud or edge deployments.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Build, retrain, validate, and maintain data-driven models for sports, concerts, and live events. Investigate data quality issues and unexpected model behavior, fulfill recurring and ad hoc modeling requests, and use Python and SQL to manipulate datasets and troubleshoot workflows. The role also supports internal application development, debugging, automation, process improvements, and cross-functional collaboration to deliver accurate and reliable modeling outputs.

Analyzes large business datasets using Python, SQL, machine learning, and NLP to classify companies by NAICS codes, identify market opportunities, and support lead generation. Develops automated classification processes, predictive models, data quality improvements, dashboards, and reports. Collaborates with sales, marketing, business development, and leadership teams to deliver actionable insights.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Build geospatial predictive risk models using LiDAR, GIS, terrain, and weather data to simulate utility-grid risks, including wildfire and extreme-weather impacts. Develop spatial data pipelines, integrate diverse datasets, establish spatial QA/QC standards, translate risk metrics into investment insights, collaborate with utility GIS teams, and mentor others in data science and analytics.

Develop data pipelines, machine learning models, statistical algorithms, and physics-informed models for downhole tools, signal processing, and formation evaluation. Analyze large structured and unstructured datasets, simulation outputs, and experimental data. Build analytics tools, automate workflows, and collaborate with engineers, physicists, and product teams to support tool development. Communicate technical findings, contribute to project planning and documentation, and apply oil and gas domain expertise to improve system understanding.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.

Build and maintain AgencyBloc’s revenue data platform in Databricks, including pipelines, automated reporting, dashboards, data models, quality checks, and reconciliation across GTM systems. Support forecasting, commissions, and predictive analytics while documenting metric definitions and governance standards. Partner with sales, client success, finance, and executives to explain findings, align revenue metrics, and deliver a governed source of truth.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Develop reproducible emissions calculations and analytical methodologies using Python, SQL, and PySpark. Investigate complex energy and emissions datasets, identify data-quality issues, apply statistics and machine learning, and translate research into scalable production capabilities. Collaborate with engineering, product, research, and customer teams while contributing maintainable code through Git, testing, documentation, and code review.