Top Data Science Jobs in Houston, TX

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

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.

Design, research, and deploy NLP/GenAI surveillance models (LLMs, RAG, knowledge graphs) to detect suspicious financial behavior; build production-ready systems, collaborate with Compliance/Legal/IT, and drive adoption of AI solutions.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead data science partnership for Mozilla’s advertising business, identifying high-impact opportunities and owning complex analytics end to end. Develop measurement frameworks, experimentation strategies, instrumentation, and reporting for advertising products. Analyze revenue and performance, evaluate trade-offs, and communicate clear recommendations and uncertainty to technical teams and senior executives.

Develops retention, churn, and customer lifetime value models using statistical analysis and machine learning. Responsibilities include extracting and preparing structured and unstructured data, performing ad hoc analyses, creating dashboards and reports, automating analytical procedures, monitoring implemented models, and presenting findings to cross-functional business, IT, and management teams.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Develop and manage production-grade machine learning models for wholesale power and gas markets, including price forecasting, congestion analysis, load forecasting, and trading algorithms. Own the ML lifecycle from feature engineering and model selection through deployment, monitoring, and refinement. Analyze ERCOT and PJM market drivers, build data visualizations, apply LLMs, collaborate with technical and business stakeholders, and communicate insights that support trading, risk mitigation, and operational decisions.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Own applied statistical analysis for mineral processing operations, including experimental design, metric definitions, uncertainty quantification, process control, forecasting, reporting, and dashboards. Analyze messy sensor, assay, laboratory, and plant data to support production, cost, supplier, and capacity decisions. Partner with data platform teams, improve analytical rigor across the company, and communicate findings clearly to operators, engineers, and executives.

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.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Leads advanced analytics initiatives supporting strategic planning, equity tracking, forecasting, budgeting, staffing, and resource allocation. Builds predictive models, simulations, and decision-support tools using education and enterprise data. Presents insights to executive and board-level audiences, partners with academic, operational, and finance teams, maintains reproducible workflows and data governance compliance, collaborates on data quality, and mentors analysts.

Design, deploy, and optimize production machine learning and data science solutions across the full lifecycle. Develop predictive models, analytical pipelines, and scalable machine learning applications; monitor model performance; and translate business challenges into actionable insights. Collaborate with engineers, developers, product managers, stakeholders, and cross-functional Agile teams while working with large structured and unstructured datasets, cloud platforms, data warehouses, and distributed processing technologies.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.