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.

DOF’s 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 is seeking a Senior Data Analytics Specialist to serve in the Property Data Analytics Report Plan Group. The ideal candidate will provide research and analytical support for executive initiatives by operationalizing new workflow techniques, providing tools to assessors and field valuation that leverage technology through statistical summaries, designing summary data analyses and quality control reports based on extremely large datasets, property valuation, modeling, spatial analyses, and map driven data pulls.

Reporting to the Director of the Property Data Analytics Report Plan Group, the selected candidate’s duties and responsibilities will include, but are not limited to the following:

- Supervise and mentor one staff member across a variety of operational and innovation-driven projects.
- Lead the modernization and optimization of the annual NYC Rent Guidelines Board (RGB) data-formatting process to enhance accuracy, efficiency, and usability.
- Serve as a key contributor in designing and developing internal web pages that strengthen and streamline property valuation workflows.
- Enhance data-integration capabilities by refining workflows for acquiring GeoBin and GeoDist data from the NYC Department of City Planning (DCP) and updating downstream reporting systems.
- Build and deploy an automated, Python-driven solution for updating the City Hall Briefing Deck, significantly reducing manual work and improving reporting timeliness.
- Assume full ownership of RPIE data-mapping processes and associated reporting projects, including ongoing maintenance, updates, and continuous process improvements.
- Develop and apply machine learning models to identify data anomalies and validate data integrity across multiple reports and databases.

Additional Information:

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire.

This position may be eligible for remote work up to 2 days per week, pursuant to the Remote Work Pilot Program agreed between the City and the Collective Bargaining Unit representing employees serving in the civil service title.

BUSINESS RESEARCH & DATA ANALY - 13135

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.

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.