Job Title: Data Science Internship
PVN ID: VB-2402-006127
Category: Research
Location: OFFICE OF SR VC-BUDGET, FINANCE & FISCAL POLICY

Job Description

General Description

CUNY Institute for State and Local Governance (ISLG): About Us 

The CUNY Institute for State and Local Governance has a mission to improve the financing, delivery, and measurement of critical public services through research, technical assistance, and education. We lead projects aimed at reforming the criminal justice system, measuring urban inequality, expanding the reach of social service providers, and more. Each project has a dedicated team of research and policy experts who collaborate closely with internal and external organizations. For more information about ISLG, please visit www.islg.cuny.edu. 

Our Goals for Diversity and Inclusion 

We strive to foster an office environment and an approach to work that welcomes and respects different perspectives, backgrounds, and life experiences. We are working towards our goal of recruiting and retaining staff, interns and advisory group members who are diverse in terms of race, national origin, sexual orientation, gender identity or expression, age, religion, veteran status, socioeconomic status, disability, and justice system involvement. 

Who we are looking for

ISLG is recruiting up to two (2) CUNY undergraduate and/or graduate student data science interns to support the John D. and Catherine T. MacArthur Foundation’s Safety and Justice Challenge (SJC), an initiative to reduce over-incarceration by changing the way America thinks about and uses jails. ISLG serves as the primary data and research partner for the SJC. Among ISLG’s responsibilities is providing data, measurement, and analytic support to participating cities and counties across the country. This support includes the collection, cleaning, validation, and management of administrative case-level data collected from across each city and county’s criminal legal system, and contributes to the broader research and evaluation agenda of the Challenge.

The interns will help develop a comprehensive data tree, utilizing data science techniques and approaches to identify and map data across SJC jurisdictions’ criminal legal data systems. Interns will work closely with core data and research staff to translate practical data analysis needs and supports into programming work – this may include 1) developing syntax to process, validate, and standardize administrative data files and 2) conducting quantitative analysis (descriptive statistics, etc.) across administrative data files.

The core of this internship is creating community and peer-to-peer learning among ISLG interns across Institute departments; to foster community we will have bi-weekly, in-person convenings that will cover a range of topics such as relationship building, project management, etc.

We are hiring interns to work at ISLG for 10 weeks during the summer months. Interns will have a hybrid work schedule and report to a designated ISLG staff member. Interns will work on a range of tasks within the project(s) listed above as well as others not listed here. Tasks that the interns may be responsible for include:

  • Apply data science principles to identify, connect and standardize data across data systems and system points;
  • Conduct data diagnostics for review by senior data staff members;
  • Document data management decisions and technical work;
  • Assist in the development of a comprehensive data tree for mapping data across various data systems;
  • Write syntax to process, validate and standardize case-level administrative data files for analysis;
  • Create supporting data visualizations to communicate analytical findings;
  • Other tasks as needed to support ISLG’s work.

Other Duties

Qualifications

Qualifications

  • Currently enrolled in one of the following degree programs at a CUNY school or have graduated from such a program within the last year: computer science, data analytics, data science, data/information management, economics, or related field.
  • Possess demonstrated experience carrying out tasks related to the collection, management, and dissemination of complex source data (i.e., writing syntax to import, validate, normalize, merge, restructure, and/or clean data files for analysis).
  • Strong proficiency required in one or more of the following programming languages: Python, R, and/or SQL.
  • Ability to communicate technical work and with team members in an accessible manner.

We would love to hear from you, if you are/have:

  • Ability to commit to a consistent schedule of approximately 35 hours per week for the internship period (i.e., May 28 – End of July)
  • Ability to attend 10 peer-to-peer credentialed in-person weekly sessions on Wednesday’s from 9:30am-11:30am
  • Excellent time management skills and experience working in deadline-driven environments
  • Ability to prioritize and work on a number of tasks simultaneously
  • Flexibility about projects and workflow 
  • Passion about equitable state and local policy solutions to pressing social issues 

ISLG is open to flexibility on the requirements above, but we would expect candidates to fit most of the items described.

How to Apply

To apply for an internship, you will need to submit the following:

  1. A cover letter that includes your area(s) of interest, how you hope to contribute to ISLG’s mission, and your proposed start and end dates and work schedule. (1 page limit)
  2. A resume or curriculum vitae. (1 page limit)
  3. The contact information for 2 references (preferably professors or former supervisors of employment relating to the work of ISLG).
  4. Unofficial transcripts (optional but strongly recommended).

All applicants will receive an additional ISLG’s Internship Program application form (which will be emailed to all candidates).

All application documents must be in English and submitted in a single PDF to info@islg.cuny.edu by the deadline listed below. Please use the subject line “Summer Student Internship Application.” Incomplete application packets will not be considered.

Application review will commence on March 25, 2024 and continue until the positions are filled.

About the Research Foundation

The Research Foundation of The City University of New York (RFCUNY) was established as a not-for-profit educational corporation chartered by the State of New York in 1963. RFCUNY supports CUNY faculty and staff in identifying and obtaining external support (pre-award) from government and private sponsors and is responsible for the administration of all such funded programs (post-award).

RFCUNY stands between CUNY’s principal investigators (PIs) and the sponsors who support them and strives to fulfill its essential responsibilities to both groups. Working closely with individual PIs and Grants Officers on the campuses, RFCUNY oversees employment, accounting, audit, reporting, purchasing, and special responsibilities that include management of a planned giving program; liaison with governmental agencies and foundations; negotiation of agreements; facility construction and renovation; protection and commercialization of intellectual property; and compliance with applicable standards in research involving human subjects, animal care, environmental and radiological safety, and conflicts of interest.

Equal Employment Opportunity Statement

The Research Foundation of the City University of New York is an Equal Opportunity/Affirmative Action/Americans with Disabilities Act/E-Verify Employer. It is the policy of the Research Foundation of CUNY to provide equal employment opportunities free of discrimination based on race, color, age, religion, sex, pregnancy, childbirth, national origin, disability, marital status, veteran status, sexual orientation, gender identity, genetic information, marital status, domestic violence victim status, arrest record, criminal conviction history, or any other protected characteristic under applicable law.

Key Features

Department
CUNY Institute for State and Local Gover
Status
Full Time
Pay Range
$20.00-$25.00
Closing Date
Apr 13, 2024 (Or Until Filled)
Bargaining Unit
No

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