Top Senior Machine Learning Jobs in Dallas, TX

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Lead and develop a team delivering data conversion for customer implementations. Drive adoption of AI and automation, manage complex/at-risk migrations, track KPIs, and improve processes through experimentation and transformation.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Design, build, and maintain large-scale data infrastructure and pipelines (data lakes/warehouses). Integrate diverse data sources, optimize processing and queries, enforce data quality and security, implement monitoring/alerting, troubleshoot issues, and collaborate with cross-functional teams while exploring new data frameworks and cloud services.

Lead design, implementation, and scaling of enterprise Palantir data solutions. Oversee cross-functional teams to build data pipelines, integrations, governance, and advanced analytics (ML/AI). Manage project delivery, stakeholder engagement, performance optimization, and mentor senior engineers and analysts while ensuring security and regulatory compliance.

Implement, install and administer an advanced analytics platform and workspaces (SAS, Tableau, R, ML). Ensure IT policy compliance and service levels, support connectivity to data sources (Hadoop, Oracle, SQL Server), perform capacity planning, performance tuning, document procedures, troubleshoot issues, coordinate implementation with architects and service providers, and automate platform administration with scripting.

Lead end-to-end analytics for Flagship Client and Distribution: partner with stakeholders to frame problems, perform advanced analyses and machine learning, ensure data quality, develop recurring analytics products and executive visualizations, and mentor analysts to drive data-driven decisions.