NYC Department of Finance (DOF) is responsible for administering the tax revenue laws of the city fairly, efficiently, and transparently to instill public confidence and encourage compliance while providing exceptional customer service.

Property Valuation and Tax Mapping is responsible for valuing more than one million properties in the city and overseeing assessments and valuations in a fair and equitable manner. The valuations enable the efficient and effective collection of property tax revenues for New York City.

The Property Division at DOF seeks an individual with strong statistical and GIS analytical skills to serve as Data Scientist in the Division's Property Modeling and Research Group. The research group is responsible for the mass appraisal of New York City's residential and commercial real estate. The selected candidate will assist with the development of computer programs and data models, provide support to other modelers, and work closely with management.

Duties will include, but not limited to:

- Manage and manipulate large datasets in various formats, performing data cleaning and quality assessments to ensure accuracy and consistency. Critically analyze results to support multiple projects.
- Conduct in-depth research and analysis of property-related data, including sales, costs, market trends, economic conditions, and other factors affecting property values, using statistical analysis and econometric modeling techniques.
- Perform spatial analysis to evaluate the impact of location on property values.
- Develop, test, and implement models to estimate property values across diverse property types.
- Support business functions by providing ad-hoc data summaries through tables, graphs, trends, charts, and maps to convey insights.
- Design and develop property valuation dashboards, applications, and tools for both internal and public use.

Level I

1. A master’s degree from an accredited college or university in social science, economics, statistics, computer science, data analysis, geography, sciences, technology, engineering, mathematics (STEM), or a closely related field, with at least 12 credits or five courses in economics, public policy, econometrics, statistics, mathematics, engineering, geography or computer science.

2. A baccalaureate degree from an accredited college or university as described in “1” above and two years of full-time, professional experience performing statistical analysis and programming work in any of the areas described in “1” above.

Level II

1. A master’s degree from an accredited college or university in social science, economics, statistics, computer science, data analysis, geography, sciences, technology, engineering, mathematics (STEM), or a closely related field, with at least 12 credits or five courses in economics, public policy, econometrics, statistics, mathematics, engineering, geography or computer science.

2. A baccalaureate degree from an accredited college or university as described in “1” above and two years of full-time, professional experience performing statistical analysis and programming work in any of the areas described in “1” above.

Special Note:

To be eligible for placement in Assignment Level II, individuals must have, in addition to meeting the minimum requirements, at least one year full-time work experience in a related field, or a master’s degree from an accredited college or university, in the areas described in “1” above, with at least 12 credits or three advanced courses in economics, public policy, econometrics, statistics, mathematics, engineering, geography, or computer science."

Level III

1. A master’s degree from an accredited college or university in social science, economics, statistics, computer science, data analysis, geography, sciences, technology, engineering, mathematics (STEM), or a closely related field, with at least 12 credits or five courses in economics, public policy, econometrics, statistics, mathematics, engineering, geography or computer science.

2. A baccalaureate degree from an accredited college or university as described in “1” above and two years of full-time, professional experience performing statistical analysis and programming work in any of the areas described in “1” above.

Special Note:

To be eligible for placement in Assignment Level III, individuals must have, in addition to meeting the minimum requirements of Level II, at least three years full-time work experience in a related field, or a Doctorate degree from an accredited college or university, in the areas described above, with at least 12 credits or three advanced courses in economics, public policy, econometrics, statistics, mathematics, engineering, geography or computer science.

- Proficiency in SAS or other programming languages (e.g., Stata, R, SPSS, or similar). - Experience with statistical and econometric modeling techniques, including regression analysis. - Strong programming skills with expertise in analyzing large datasets. - Familiarity with ArcGIS or other geographic information system (GIS) software. - Experience with Power BI or other data visualization tools. - Experience with relational databases, data mining, and data extraction using SQL. - Strong interpersonal and teamwork skills, with the ability to work independently. - Excellent quantitative analytical skills with strong attention to detail. - Proficiency in Microsoft Word, Excel, and PowerPoint.

As a prospective employee of the City of New York, you may be eligible for federal loan forgiveness programs and state repayment assistance programs. For more information, please visit the U.S. Department of Education’s website at https://studentaid.gov/pslf/.

New York City residency is generally required within 90 days of appointment. However, City Employees in certain titles who have worked for the City for 2 continuous years may also be eligible to reside in Nassau, Suffolk, Putnam, Westchester, Rockland, or Orange County. To determine if the residency requirement applies to you, please discuss with the agency representative at the time of interview.

The City of New York is an inclusive equal opportunity employer committed to recruiting and retaining a diverse workforce and providing a work environment that is free from discrimination and harassment based upon any legally protected status or protected characteristic, including but not limited to an individual's sex, race, color, ethnicity, national origin, age, religion, disability, sexual orientation, veteran status, gender identity, or pregnancy.