Patron Insights Researcher, Quantitative Research

The New York Public Library • New York, NY

2d • $90,000 - $100,000 • Hybrid

About The Position

The Patron Insights Researcher, Quantitative Research supports institution-wide efforts to capture, amplify, and generate insights from patron data to expand the New York Public Library’s impact and reach its growth goals. As part of the Patron Insights & Analytics team, the role helps ensure the Library’s policies, programs, outreach, and marketing are grounded in an understanding of its patrons.

The researcher conducts ongoing quantitative research, translating programmatic and operational questions into actionable insights through end-to-end analysis of available data. The role also collaborates with the PI&A team on quantitative analyses of qualitative research and works cross-functionally with Library staff.

Within the first month, the researcher is expected to learn the Library’s organizational structure and services, become familiar with patron-research data systems and datasets, review current analyses and research goals, and meet key stakeholders. Within three months, the researcher is expected to understand the data systems and datasets, contribute to the PI&A codebase and analyses, and own one or more data-processing pipelines. After six months, the researcher is expected to lead multiple independent analyses and manage the transition of multiple pipelines to a new data lake.

The position is full-time, based at the Stephen A. Schwarzman Building, and reports to Sarah Rankin. It is a hybrid role with three days onsite and two days remote. The schedule is Monday through Friday, 9 a.m. to 5 p.m., for 35 hours per week. The work environment is an office setting; no physical duties or pre-placement physical are required. The position is non-union and exempt.

Requirements

• Bachelor’s degree.
• At least 3 years of hands-on professional experience in research and data analysis involving large or complex datasets.
• Proven experience processing and analyzing data with languages such as R, Python, and SQL.
• Experience meeting the research and data needs of a mission-based organization.
• Proficiency in a statistical programming language such as R or Python.
• Proficiency with relational database concepts and query languages such as SQL.
• Comfort with statistical techniques including significance testing, correlation, regression, distributions, factor and cluster analysis, sampling methodology, and basic predictive modeling.
• Experience creating reproducible data analyses with documentation and methodological transparency.
• Demonstrated ability to use statistical, analytical, and visualization skills to generate insights.
• Ability to independently produce presentation-ready deliverables and communicate analytical insights and methodologies clearly and persuasively, verbally, visually, and in writing.
• Strong analytic and quantitative reasoning, including the ability to evaluate rigorously what data is needed to answer a question and what questions a dataset can answer.
• Learning mindset and appetite for developing skills and absorbing information.
• Demonstrated ability to collaborate effectively and establish trusting relationships across a diverse organization and multiple levels of management and staff.

Nice To Haves

• Experience working with survey data.
• Understanding of public libraries and research libraries.

Responsibilities

• Conduct in-depth analyses of library datasets to develop insights about patron usage and metrics for ongoing institutional tracking.
• Conduct targeted tactical analyses and create data summaries in response to requests from Library departments.
• Communicate findings to stakeholders and help non-technical audiences interpret complex results.
• Collaborate with PI&A colleagues to build and maintain a shared analytical codebase using R or Python and version control.
• Maintain PI&A data-processing and analytical pipelines during the transition to a more robust data lake.
• Build and maintain survey-data analysis pipelines to support rigorous and consistent analysis of information collected from program participants.
• Collaborate with the data engineering team to refactor existing metric pipelines and build new pipelines in the data lake infrastructure.
• Perform other responsibilities as required.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level