Top Entry Level Data Science Jobs in Houston, TX

Build and deploy data science models for well integrity, pressure-test interpretation, BOP predictive maintenance, annulus pressure monitoring, and barrier assurance. Clean and structure operational data, communicate model results and limitations to engineering and leadership teams, and support real-world drilling and completion decisions. The role requires wells or well integrity engineering experience, practical data science skills, statistics, and demonstrated use of an analytical model in engineering or operational decisions.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

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.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Leads the design, development, and deployment of production-scale AI and machine learning systems. Builds solutions using LLMs, generative AI, NLP, computer vision, and deep learning; architects ML pipelines; optimizes models through fine-tuning and prompt engineering; translates technical results into business strategies; partners with stakeholders; communicates outcomes; and mentors data scientists while establishing MLOps best practices.

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.