Top Senior Analytics 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.

Supports credit risk valuation and decision science for consumer lending portfolios. Maintains and deploys financial valuation models, performs quarterly FAS107 asset valuation, analyzes forecasting and reporting data, reconciles production outputs, documents governed models, and resolves modeling issues. The role applies credit card strategy, P&L modeling, statistical analysis, and portfolio analytics while leading initiatives, mentoring staff, and communicating insights to senior stakeholders.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Leads data engineering and analytics initiatives supporting marketing science. Evaluates complex data pipelines, data quality, and stakeholder requirements; develops data products, controls, validation checks, and automation using cloud technologies. Performs analysis for marketing strategy, translates business needs into technical solutions, identifies engineering improvements, and facilitates cross-functional technical discussions. The role also requires documentation, stakeholder management, compliance adherence, and end-to-end ownership of analytics initiatives.

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.

Lead analytics for small business insurance marketing by developing audience segments, targeting strategies, campaign measurement, and experimentation plans. Partner with marketing, insurance, analytics, and technology teams to align data definitions, build scalable data assets, diagnose analytical issues, and deliver executive-ready insights. Use Python, SQL, cloud platforms, and modern data warehousing to improve campaign performance, conversion, incrementality, and decision-making.

Owns data strategy, governance, quality, protection, documentation, and delivery for a credit card product. Partners with business, analytics, technology, risk, and operations teams to define data requirements, deploy data solutions, manage workstreams and KPIs, mitigate data risks, and ensure compliance with firmwide standards. Leads direct or matrixed staff and provides subject matter expertise across data management, engineering, architecture, pipelines, modeling, and analytics.

Supports portfolio planning and operational analytics for auto insurance program delivery. Analyzes capacity, forecasts, rollout sequencing, dependencies, and performance metrics; builds dashboards, schedules, and reporting tools; conducts scenario analysis; and recommends delivery trade-offs. Partners cross-functionally with program management, state management, data science, and technology teams to maintain realistic roadmaps and improve planning workflows using Power BI, Power Apps, JIRA, Excel, AI, and automation.

Leads end-to-end marketing analytics for small business acquisition campaigns, including opportunity sizing, targeting, experimentation, performance reporting, profitability, and ROI measurement. Partners with Marketing, Finance, Modeling, and Data teams to guide investment decisions and improve measurement rigor. Owns stakeholder delivery, data requirements, analytic frameworks, controls, and auditability while coaching junior analysts and translating complex findings into clear recommendations for senior audiences.

Leverage data analytics, business intelligence, and data science techniques to deliver strategic insights and solutions for clients. Build reports, dashboards, data pipelines, visualizations, and data architecture using tools such as Oracle BI, QlikView, Spotfire, and BIRT. Ensure data quality, manage stakeholder relationships, improve analytics processes, interpret complex datasets, and mentor junior team members while contributing to client projects and business strategy.

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.

Support operational reporting and analytics for Payments Operations by maintaining dashboards, scorecards, metrics, and scheduled reports. Perform SQL-based data validation, reconciliations, issue triage, root-cause coordination, ad hoc analysis, documentation, and control checks. Partner with Operations, Technology, and Data teams to improve reporting reliability, automate manual processes, strengthen governance, and communicate findings and risks to stakeholders.

Design and execute complex analyses evaluating property claims operations, process changes, tools, and vendor partnerships. Build reporting frameworks, synthesize multi-source data, and translate findings into actionable recommendations for senior stakeholders. Independently lead analytical projects that improve financial outcomes, claims handling, customer results, and operational efficiency.

Analyze credit expansion proposals, portfolio performance, loss forecasts, and reputational risk using internal, external, bureau, and deposit data. Develop strategy frameworks, identify emerging risk trends, diagnose performance anomalies, and recommend policy improvements. Extract and reconcile large datasets using SAS and SQL, communicate findings to senior stakeholders, validate peer analyses, and mentor junior team members.

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.

Analyzes healthcare, financial, and utilization data to identify inefficiencies, support business decisions, build and maintain dashboards, develop analytical models, and communicate actionable insights to technical and non-technical stakeholders.

Provides customized healthcare operations analytics using Vizient and hospital data. Analyzes performance, costs, productivity, utilization, outcomes, supply use, and benchmarks; develops reports and actionable insights for hospital leaders. The role facilitates client meetings, translates findings into strategic and operational recommendations, ensures reporting accuracy, and contributes to mentoring and team best practices.

Leads pricing analytics initiatives, develops strategies for risk-based pricing, collaborates with leaders, prioritizes analytics projects, and enhances customer experience.

Leads BI, analytics, and AI initiatives that transform energy market and customer data into dashboards, automated insights, predictive analyses, and strategic recommendations. The role supports pricing, acquisition, retention, marketing, finance, and customer experience decisions through Python, SQL, Tableau, Power BI, generative BI, experimentation, and machine learning. Responsibilities include building executive reporting, evaluating AI-enhanced BI platforms, analyzing customer lifecycle performance, mentoring analysts, and partnering with cross-functional business and technical teams.

Analyze large datasets to identify opportunities that improve customer experience and profitability. Build and automate reporting, develop quantitative analyses and visualizations, and provide strategic recommendations to senior leaders. The role uses Excel, SQL, Snowflake, R, and Tableau while collaborating across teams, managing multiple project streams, and designing and implementing new products and solutions.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Leads and develops a team of data scientists supporting Tinder’s Integrity organization. Partners with product leadership across safety, account integrity, anti-abuse, and moderation. Owns analytical strategy, experimentation, causal inference, measurement frameworks, high-stakes investigations, and executive-level recommendations. Requires hands-on expertise with complex datasets, classifier evaluation, and trust and safety or adversarial behavior domains.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Leads the Data Engineering and Analytics team, managing people, technical strategy, intake prioritization, data pipelines, governed reporting, and stakeholder delivery. Oversees Python, SQL, dbt, Airflow, and BigQuery data products while guiding predictive analytics and machine-learning initiatives such as forecasting, recommendations, and segmentation. Establishes quality, reliability, governance, and development standards; mentors team members; supports debugging, process improvement, monthly reporting, invoices, and scorecards. The role also promotes responsible AI adoption and PHI-compliant data practices.

Designs, builds, tests, and supports enterprise data pipelines and Snowflake archival solutions for acquisitions, integrations, reporting, compliance, and analytics. Translates business and technical requirements into mappings, transformations, quality rules, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams. Oversees validation, monitoring, documentation, cutover, go-live readiness, hypercare, and operational handoff while improving reusable, secure, cost-efficient data engineering practices.