Top Data Science Jobs in Houston, TX

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 and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

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.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

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.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Design and deploy predictive forecasting and analytics to support Revenue operations. Build models for forecasting, churn, expansion, and pipeline analytics; create scalable dashboards; partner with Revenue Operations, Finance, and Data Engineering to improve data quality, KPI governance, and executive reporting to drive data-driven decisions and AI readiness.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

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 lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, build, and deploy machine learning and generative AI models (including LLMs and text-to-image). Analyze large datasets, run A/B and statistical tests, collaborate with data engineering and engineering teams, and create dashboards/visualizations to communicate insights. Optimize data pipelines and model performance to support data-driven decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.

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.

The role involves building and scaling a Closed-Loop Design Program that enhances pre-sales engineering through data science and analytics in supply chain automation.