Top 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.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Leads Vizient’s indirect spend data and analytics strategy, operating model, governance, quality standards, reporting, automation, and AI adoption. Oversees data lifecycle management, modeling, integration, categorization, normalization, analytical methodologies, dashboards, and decision-support tools. Manages data and analytics teams, develops reusable assets for consulting engagements, partners with technology and product teams, and supports business development and member presentations.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

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.

Nine- to ten-week summer associate internship for advanced-degree students. Quantitative strategists apply mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting financial markets and firm-wide risk, compliance, treasury, and reporting processes. Participants receive training, collaborate with experienced professionals, and work on real responsibilities using modern engineering, cloud, AI, and machine learning capabilities.

Summer Analyst internship for students pursuing a bachelor’s or graduate degree. Quantitative strategists apply advanced mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting global financial markets and core financial operations. Interns receive training, attend orientation, and work on real responsibilities alongside experienced professionals in a fast-paced environment.

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.

Support analytical projects across Gartner's BTI and Product teams: analyze large datasets, build dashboards and reports, apply ML/AI and LLM tools, assist data pipeline setup, collaborate with data engineering and IT, and communicate insights to stakeholders. High-performing interns may receive full-time offers.

Entry-level Associate Data Analyst supporting analytics, data science, data engineering, and project management rotations. Deliver insights, build dashboards, automate reporting, partner with stakeholders, and apply ML/LLM tools to drive product and client analytics.

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.

Leads enterprise data architecture strategy and governance for Citi’s fraud domain. Responsibilities include developing data models, flows, standards, and remediation frameworks; architecting scalable data solutions; supporting analytics and AI use cases; partnering with senior stakeholders; and managing data architecture analysts. The role requires extensive financial services experience, fraud-domain expertise, enterprise data architecture capabilities, executive communication, and cross-functional leadership.

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.

Build predictive data pipelines, machine learning models, dashboards, and automated decision tools for portfolio strategy. Analyze product performance, costs, monetization, competitive intelligence, customer behavior, and feature opportunities. Govern metrics and data quality using SQL while enabling self-service AI and natural-language data access. Support roadmap prioritization, ROI assessment, initiative tracking, and executive decision-making through advanced analytics and financial modeling.

Design, develop, and maintain ETL pipelines, databases, and data visualizations to support 300mm semiconductor manufacturing. Analyze operational performance, automate data extraction, troubleshoot systems, collaborate with stakeholders, and integrate emerging technologies to improve manufacturing efficiency.

Lead development, validation, and operationalization of P&C actuarial models and workflows. Convert actuarial requirements into production-ready code, leverage cloud/AI tools, validate data pipelines, produce client-ready reporting, and liaise between actuarial SMEs and engineering teams while contributing to practice tooling and business development.

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.