Some of the examples of work but not limited to, in this role are:
Data Quality Assessment - At intake, the Data Scientist shall assess the quality of the data. Data quality assessment is a continual process and should include qualitative and quantitative assessment to include but not limited to summarizing all variables and providing statistics such as minimum, maximum, mean, median and number of missing values. Prior to providing preliminary and final results, the Data Scientist is responsible for conducting a data quality assessment to resolve all errors and data inconsistencies.
Data Cleaning - The process of transforming, parsing, aggregation and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of this process is to ensure the quality and usefulness of data. The process goal is for analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data.
Data File Review and Cleaning Results - The Contractor shall document its understanding of each file review and cleaning task in its project management plan. During the file review the Contractor shall scrutinize the accuracy of large loan file submissions from respondents and summarize observations in a memo. This summary should include, but is not limited to, missing values, logically inconsistent values, or implausible values by comparing it to other sources of information, such as underlying loan documents. Subsequently, the Contractor shall clean data by handling missing values, logically inconsistent values, implausible values and as well as recode, create, or impute any variables needed. When required, the Contactor shall merge multiple databases, for example, combining a respondents administrative data with U.S. Census or Home Mortgage Disclosure Act (HMDA) data. The contractor shall provide a detailed data file that allows the government to review and replicate steps taken to analyze and clean datasets. Examples of steps taken include inputting data, merging datasets, creating variables, and conducting analysis. The contractor shall have the capability to perform data analysis to achieve the performance objectives in the PWS utilizing the following software:
Data Analysis Reports Review:
Requirements:
Experience working with Housing and Urban Development is preferred.
3 years professional experience in the field.
Should be proficient in using SAS and R or STATA software for statistical and regression analysis.
Job Type: Full-time
Pay: $113,000.00 - $143,000.00 per year
Benefits:
Experience level:
Schedule:
Ability to Relocate:
Work Location: In person
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