Lead Advanced Analytics

Lead Advanced Analytics page is loaded## Lead Advanced Analytics remote type:
Office Worker (NOT Remote) locations:
Dallas, Texastime type:
Full time posted on:
Posted Todaytime left to apply:
End Date:
March 12, 2026 (13 days left to apply) job requisition :
R-87527
* REMOTE TYPE:
Office Worker:
In office position with work from home allowed, i.e. flex or hybrid worker
*
* JOB LOCATION:

* 311 S Akard St. Dallas, TX 75202           
* DUTIES:
* Uses predictive modeling, statistics, trend analysis, and other data analysis techniques to collect, explore, and identify the appropriate data to be analyzed to explain or predict behavior, solve a variety of business problems, and build business cases. Responsible for business case development, big data analytics modeling using tools (ex: including but not limited to: Tableau, Palantir, Power Bi, Databricks, Aws Azure, Synapse, H2O Driverless AI, etc.),

and business process integration. Gathers, analyzes, and interprets a wide variety of data to identify causal relationships, trigger points and ultimately make predictions. Builds predictive models using data, tests the model on results outside of the sample size, and verifies the model in the real world. Quantifies significance of data variances, applies an array of statistical methods ranging from traditional to newly developing within the big data space.

Use traditional modeling techniques to build models that support decision making. Develop reports, charts, tables, and other visual aids in support of findings. Advises business partners with regard to patterns and relationships in data to recommend business direction or outcomes. Work with mapping spatial and analytics technology including geographic information system (GIS) supported by ESRI. Utilize ESRI ArcGIS Pro, ArcGIS Online, Arc Py, ESRI Dashboards.

Create static and interactive maps. Utilize Spatial joins, buffer analysis, and proximity analysis. Utilize Python (Pandas, Geo Pandas, PySpark) and SQL within Databricks and Snowflake environments. Operate GIS Tools and Technologies, including Feature classes, shape files, WKT/WKB, projections. Use GeoJSON, PostGIS, PgAdmin. Utilize visualization tools:

Power BI, ESRI Dashboards and Viewers. Employ Cloud and Big Data Platforms:

Azure Synapse, Palantir Foundry. Use ETL Tools:
Alteryx, Azure Data Factory. Create and visualize Geospatial layers using ESRI tools. Utilize and transform data in different formats to ensure the data can be utilized for Geospatial analytics.  Creation of Geospatial analytics on adhoc requests driven from business requests. Create and monitor ESRI Geospatial dashboards. Design static and dynamic mapping application keeping best practice design in mind. Utilize ETL tooling to translate between traditional coding methods and code free workflows.

Utilize geospatial analysis methodologies to determine relationships of various types of layers and their relationships to one another. Interface with internal clients to understand requests, requirements, and complete requested work.
* REQUIREMENTS:
* Requires a Bachelor’s degree, or foreign equivalent degree in Engineering, Data Science, or Computer Science and five (5) years of progressive, post-baccalaureate experience in the job offered or five (5) years of progressive, post-baccalaureate experience in a related occupation working with mapping spatial and analytics technology including geographic information system (GIS) supported by ESRI; utilizing ESRI ArcGIS Pro, ArcGIS Online, Arc Py, ESRI Dashboards;

creating static and interactive maps; utilizing Spatial joins, buffer analysis, and proximity analysis; utilizing Python (Pandas, Geo Pandas, PySpark) and SQL within Databricks and Snowflake environments; operating GIS Tools and Technologies, including Feature classes, shape files, WKT/WKB, projections; using GeoJSON, PostGIS, PgAdmin; utilizing visualization tools:

Power BI, ESRI Dashboards and Viewers; employing Cloud and Big Data Platforms:

Azure Synapse, Palantir Foundry; using ETL Tools:
Alteryx, Azure Data Factory; and utilizing and transforming data in different formats to ensure the data can be utilized for Geospatial analytics.

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