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Senior data scientist vs geospatial scientist

The differences between senior data scientists and geospatial scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a senior data scientist and a geospatial scientist. Additionally, a senior data scientist has an average salary of $124,093, which is higher than the $111,782 average annual salary of a geospatial scientist.

The top three skills for a senior data scientist include python, data science and visualization. The most important skills for a geospatial scientist are GIS, R, and python.

Senior data scientist vs geospatial scientist overview

Senior Data ScientistGeospatial Scientist
Yearly salary$124,093$111,782
Hourly rate$59.66$53.74
Growth rate16%16%
Number of jobs103,61842,718
Job satisfaction--
Most common degreeBachelor's Degree, 40%Bachelor's Degree, 60%
Average age4141
Years of experience44

Senior data scientist vs geospatial scientist salary

Senior data scientists and geospatial scientists have different pay scales, as shown below.

Senior Data ScientistGeospatial Scientist
Average salary$124,093$111,782
Salary rangeBetween $90,000 And $170,000Between $74,000 And $168,000
Highest paying CityRichmond, CA-
Highest paying stateWashington-
Best paying companyBrex-
Best paying industryStart-up-

Differences between senior data scientist and geospatial scientist education

There are a few differences between a senior data scientist and a geospatial scientist in terms of educational background:

Senior Data ScientistGeospatial Scientist
Most common degreeBachelor's Degree, 40%Bachelor's Degree, 60%
Most common majorMathematicsGeography
Most common collegeColumbia University in the City of New YorkUniversity of California, Berkeley

Senior data scientist vs geospatial scientist demographics

Here are the differences between senior data scientists' and geospatial scientists' demographics:

Senior Data ScientistGeospatial Scientist
Average age4141
Gender ratioMale, 86.5% Female, 13.5%Male, 81.0% Female, 19.0%
Race ratioBlack or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage9%9%

Differences between senior data scientist and geospatial scientist duties and responsibilities

Senior data scientist example responsibilities.

  • Administrate SharePoint database access; manage classified documents and applications.
  • Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
  • Administer business intelligence systems and done business data analysis, visualization and reporting.
  • Develop MapReduce jobs in java for data cleaning and preprocessing.
  • Integrate internal and external data via API for cross platform marketing campaign evaluations.
  • Diagnose denial of service attacks and improve security using AWS security groups and network ACLs.
  • Show more

Geospatial scientist example responsibilities.

  • Develop and implement tools in both IDL/ENVI and Erdas to automate preprocessing and enhancement of imagery data for exploitation and interpretation.
  • Test multiple HSI detection algorithms against hundreds of different camouflage schemes and materials.
  • Compare the performance of multiple HSI detection algorithms to detect and discriminate among different camouflage schemes and materials.
  • Develop probabilistic models of disease progression to predict future healthcare utilization and capitation revenue.
  • Communicate defects, encounter during regression test and followed-up with developers until all issues are resolved.
  • Require to acquire geospatial information and extract essential elements from a wide array of multi-intelligence data to include Sigint and Masint.
  • Show more

Senior data scientist vs geospatial scientist skills

Common senior data scientist skills
  • Python, 14%
  • Data Science, 11%
  • Visualization, 5%
  • Java, 5%
  • Data Analysis, 4%
  • Hadoop, 4%
Common geospatial scientist skills
  • GIS, 36%
  • R, 35%
  • Python, 15%
  • Visualization, 14%

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