Top Senior Machine Learning Jobs in Dallas, TX

Owns Small Business Deposits data across sourcing, documentation, delivery, quality, protection, and governance. Partners with product, technology, analytics, operations, risk, controls, and vendors to define data assets, requirements, access controls, retention, and risk mitigation. Leads modernization into enterprise data platforms, resolves delivery issues, aligns stakeholders, and manages direct or matrixed teams delivering data-related outcomes.

Designs and maintains data pipelines supporting monthly profitability close, automates manual finance workflows, and develops machine learning solutions for quality control, anomaly detection, and reconciliations. The role extracts and delivers financial data, prototypes LLM and agentic tools, and partners with Finance, Technology, and P&A on analytics initiatives. It also communicates technical methods and results to senior business stakeholders.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

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.

Performs complex fraud analytics and financial modeling for money movement products. Uses SAS, SQL, Excel, Tableau, and databases to evaluate fraud risk, develop mitigation strategies, improve portfolio performance, and recommend policy changes. Leads reporting, strategy requirements, testing, and rollout activities, supports biometrics enhancements, and helps implement machine learning capabilities while communicating recommendations to senior management.

Build and maintain dbt data models, migrate Alteryx workflows, and manage Dagster or Airflow pipelines in Redshift. Partner with Revenue Operations stakeholders to translate data into business insights, dashboards, and KPI visibility using Tableau. Apply AI, LLMs, and machine learning through SageMaker to support predictive use cases such as churn prediction and lead scoring.

Leads enterprise data analysis, integration, governance, visualization, business intelligence, and advanced analytics initiatives. Develops scalable ETL/ELT pipelines, dashboards, predictive insights, and data quality controls across multiple platforms. Partners with business and technology stakeholders, provides technical leadership, mentors analysts, supports cloud analytics modernization, and contributes to data strategy and architecture decisions.

Leads enterprise analytics initiatives, advanced statistical analysis, predictive modeling, business intelligence, data integration, governance, and AI-enabled solutions. Advises senior leaders, establishes data strategies and analytical roadmaps, oversees dashboards and architectures, evaluates modern technologies, resolves complex data challenges, and mentors analytics professionals.

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.

Designs, administers, and evolves Fisher Investments’ Azure data warehouse and analytics platform. Responsibilities include developing cloud data solutions, setting architecture and security standards, guiding developers, troubleshooting systems, monitoring operations, planning capacity, optimizing cloud costs, and supporting BI and data delivery teams. The role also coordinates cross-functional projects, provides after-hours support, and prepares technical work plans and status reports.

Analyzes digital product performance across web properties using Adobe Analytics, Google Analytics, tagging systems, statistical modeling, and data visualization. Develops measurement requirements, tracking specifications, dashboards, and actionable insights for product and marketing teams. Troubleshoots tags and data collection, supports experimentation and quality assurance, establishes analytics standards, and mentors analysts while communicating findings to stakeholders.

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.

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.

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.