Data Scientist--Register

Job Title

Responsible for the growth of data in the statistics and reporting environment by leading the certification of data sets that are available for reporting, collaborating with IRS information technology leaders on desired new technologies and techniques to create efficient processes around data extraction, loading, transformation, governance, and cataloguing for better and reduced time to insights for the business units. Combines, analyzes, and interprets large and/or complex data sets to develop organizational solutions.

Identifies the methods, processes, algorithms, tools, and systems to extract and interpret findings from various structured and unstructured data sets related to the data science lifecycle. Responsible for the development of algorithms and/or tools to support data manipulation and processing as well as the use of data visualization techniques to articulate findings. Uses emerging technology, and various data sources to perform exploratory analyses on new and archival data sources.

Conducts in-depth analysis of data and disseminates the results of those analyses. Identifies and/or develops innovative and effective approaches and methodologies; applies advanced operations research and data science techniques for quantitative analysis, statistical analysis, forecasting, predictive modeling, prescriptive analysis, and optimization; and validates analysis and outcomes. Conducts statistical analysis to make informed projections and recommendations.

Applies professional knowledge of computer science, mathematical, statistical theories, techniques, and methods to gather, analyze, design, and construct new processes for analytical modeling, interpreting models, and/or reporting quantitative information, trends, relationships, and correlations among or within data. Advises on the potential benefits/uses of automation to improve the efficiency of program operations. Analyzes a variety of data sources to provide data- driven insights to the Data and Analytics organization to streamline processes.

Utilizes an in-depth knowledge of data science techniques and methodologies such as machine learning, predictive analysis, prescriptive analysis and optimization, and other emerging analytical techniques to assist, consult, or lead others in the application of data science techniques. Defines and documents the specific analytics approaches applied in project delivery; establishing success metrics to be used in satisfying business's requirements for project outcomes;

and establishing key performance indicators to be used in ongoing reporting that measure the value creation delivered by the project over time.