Data Science Jobs in Plano, TX

Owns the Trade and Working Capital AI use-case portfolio from ideation through delivery and value realization. Translates business needs into technical requirements, prioritizes opportunities, coordinates with AI, data science, technology, data, and infrastructure teams, and drives adoption across product, commercial, and productivity applications. The role also leads AI literacy initiatives, aligns business priorities with enterprise governance and AI programs, manages multi-workstream delivery, and reports progress and realized value to senior stakeholders.

Leads AI product solutions from pilot through production across multiple business domains. Develops AI enablement strategies, strategic and tactical roadmaps, feature backlogs, business cases, and user experiences. Partners with business stakeholders, data scientists, engineers, architects, and UX teams to prioritize needs, design solutions, manage dependencies, measure KPIs, mitigate risks, and deliver business value. Guides teams, communicates complex concepts to senior leaders, and mentors team members.

Owns and prioritizes AI product backlogs, translating business needs into features and user stories for data science and AI engineering teams. Manages delivery risks, dependencies, controls, testing, releases, and stakeholder alignment across the AI development lifecycle. Partners with cross-functional teams to deliver compliant AI solutions from pilot through production, coaches other Product Owners, and supports process improvement, change adoption, and measurable business value.

Leads AI product development across pilots, productionalization, and delivery. Owns and prioritizes backlogs, writes and accepts features and user stories, manages dependencies, risks, releases, and compliance controls, and partners with data scientists, AI engineers, business stakeholders, and delivery teams. Coaches Product Owners, translates business needs into capabilities, supports change management, and ensures predictable delivery of AI product value across the lifecycle.

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.

Leads a data science team within wealth management, delivering analytics, machine learning, experimentation, and AI solutions that improve client outcomes, product performance, and operational efficiency. Partners with product, strategy, and operations leaders to define problems, metrics, and recommendations. Oversees modeling, client behavior analysis, experimentation frameworks, executive communication, and AI use-case development while coaching team members and managing performance.

Lead data science and NLP initiatives for AI-powered products, partnering with engineers, machine learning specialists, and product managers. Build, fine-tune, evaluate, validate, and operationalize large language and machine learning models at scale. Apply advanced NLP, deep learning, and model training techniques while supporting model risk compliance. Translate technical findings into business outcomes and develop team talent.

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.

Supports data science and analytics teams by managing Jira requirements, backlogs, workflows, dashboards, model portfolios, and prioritization processes. Partners with technical teams, leadership, and stakeholders to communicate model status, risks, blockers, and priorities. The role also improves documentation and intake processes, facilitates prioritization decisions, and builds repeatable processes in a compliance-sensitive environment.

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.