Data Science Jobs in Richardson, TX

Lead enterprise product and experience initiatives, guiding cross-functional teams (product, data, design, ML, engineering) to define strategy, deliver customer-centric digital products, prototype and apply emerging AI/automation tools, and drive transformational outcomes across Capital One.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Supports business decision-making through data analysis, predictive modeling, statistical and machine learning techniques, and presentation of insights. Interns will work across marketing, lending, indirect lending, and warranties while gaining hands-on experience throughout the data science lifecycle. The role is an 11-week, paid, on-site internship based in Tempe, Arizona, or Dallas, Texas, with no relocation assistance.

Lead and deliver advanced analytics and machine learning solutions to support pricing, revenue optimization, and partner relationships. Collaborate with cross-functional teams to design, validate, and implement models at scale using large transaction datasets and modern big-data tooling. Translate technical insights into actionable business recommendations.

Lead model validation and risk quantification across business domains. Partner with data scientists, engineers, and PMs to build and challenge production models using Python, Spark, H2O, and cloud tools. Drive model governance, backtesting, and present identified model risks to executives while mentoring teams and advancing validation frameworks.

Lead multiple data engineering teams to ensure data quality, build and optimize data pipelines, ingest and curate diverse data sources, transform data for ML/analytics, productionize data science products, collaborate with IT for deployments, manage budgets/resources, and build business cases with ROI analyses.