Top Senior Machine Learning Jobs in Dallas, TX

Leads development, validation, implementation, and monitoring of credit scorecards, loss forecasting, collection, recovery, and pricing models for auto lending. Provides strategic recommendations balancing growth, profitability, risk, and regulatory compliance. Partners with Pricing, Risk, Finance, and senior leadership, translating complex statistical analyses into actionable business insights. Uses large-scale data and advanced analytics to monitor portfolio performance, identify risks, and improve forecasting accuracy.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Develop, implement, monitor, and enhance credit risk models using statistical, econometric, and machine learning methods. Analyze large datasets with Python, SQL, SAS, R, and Spark; perform forecasting, stress testing, feature engineering, and model validation. Document methodologies, support governance and regulatory reviews, communicate findings to stakeholders, and improve analytical automation and reporting processes.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Leads global customer experience analytics strategy, developing analytical frameworks, predictive models, KPI systems, dashboards, forecasting, segmentation, retention analysis, and operational insights. Partners with CX, contact center, product, data, and business leaders to improve satisfaction, efficiency, retention, and business outcomes. Manages analytics initiatives and teams while translating complex data into executive-level recommendations and narratives.

Analyzes, designs, implements, and maintains industrial IT, historian, telematics, and data systems. Partners with operations and business stakeholders to develop technical solutions, troubleshoot complex issues, manage data ingestion and transformation, maintain analytics integrity, and execute SQL-based reporting. Leads system configuration, testing, documentation, project coordination, and process intelligence initiatives while mentoring junior team members and supporting automation, IoT, AI, and machine learning capabilities.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Build and migrate production data engineering and machine learning workloads to Databricks on Azure. Assess on-premises SQL Server and SSIS systems, develop PySpark and Spark SQL pipelines, implement governance with Unity Catalog, and create reusable CI/CD assets. Work directly with Department of Veterans Affairs teams through requirements sessions, workshops, training, and documentation. Depending on focus, build ML workflows or modernize ETL, reporting, semantic models, and Power BI solutions.

Leads the Underwriting Data Engineering team and oversees the design, development, and productionization of scalable data and machine learning pipelines. Responsibilities include feature engineering, data preparation, model deployment, data validation, governance, monitoring, API integration, analytic data products, roadmap development, stakeholder collaboration, and team coaching.

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.

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.