Predictive Analytics Intern - job 1 of 2

THE SELECTED CANDIDATES WILL BE OFFERED A SALARY OF $26.23 PER HOUR.

The Administration for Children’s Services (ACS) protects and promotes the safety and well-being of children and families through child welfare and juvenile justice services and community supports. ACS manages community-based supports and foster care services and provides subsidized childcare vouchers. ACS child protection staff respond to allegations of child maltreatment. In juvenile justice, ACS oversees detention, placement, and programs for youth in the community.

ACS’ Division of Policy, Planning and Measurement (PPM) collaborates with every ACS division to bring knowledge to practice. PPM guides systems analysis and strategic systems improvement; assures quality of practice at ACS and its provider agencies; professionalizes the frontline workforce; brings knowledge into practice; provides research and analytic support; and plans and develops new programs and policies. PPM is looking for two highly motivated and detail-oriented Predictive Analytics Graduate Interns to join our team.

As a Predictive Analytics Graduate Intern, you will support the Predictive Analytics Team within the Office of Research and Analytics, which is in process of building a risk model that will identify cases most in need of investigative consultation. This internship offers an excellent opportunity to work as a member of a team to identify high-risk cases during the early stages of the investigation so that the right cases get referred and receive timely attention for the most concerning factors.

Key Responsibilities:

The model building will require the following iterative steps to be performed:

- Extensive feature engineering to identify the right factors with predictive power

- Using various imputation methods to identify and deal with missing or inconsistent data entry information

- Use different model-building methodologies to identify the right model for the problem statement and nature of data

The project will also include these additional key tasks:

- Perform forecasting and data modeling: Using appropriate methodologies, evaluating data patterns, monitoring key performance metrics, and performing root cause analysis

- Analyzing features to evaluate feature importance

- Data visualization using software such as Tableau, R, or Python

- Draft literature reviews: Research best practices about the use of predictive models in child welfare, juvenile justice, and other relevant domains such as other social services and criminal justice

- Use current research on fairness, accountability, and transparency from the machine-learning community (i.e., FATML) to inform model development and implementation

Learning Outcomes:

- Experience building data sets for exploratory analysis, and evaluating and defining performance metrics

- Gain comprehensive knowledge in building a Predictive Risk Model (PRM) for prioritizing investigative consultation

- Develop skills in building and analyzing dashboards and reports

- Enhance abilities in documenting all analyses and reports pertaining to project accomplishments


ADDITIONAL INFORMATION:

Section 424-A of the New York Social Services Law requires an authorized agency to inquire whether a candidate for employment with child-caring responsibilities has been the subject of a child abuse and maltreatment report.


TO APPLY:

- You must be a graduate student and must either be currently enrolled in a college or university or must have graduated within one year of the current program year.

- Interested candidates should submit their resume by visiting: https://cityjobs.nyc.gov and search for Job ID#707180

- NO PHONE CALLS, FAXES OR PERSONAL INQUIRIES PERMITTED

- NOTE: ONLY CANDIDATES UNDER CONSIDERATION WILL BE CONTACTED

SUMMER GRADUATE INTERN - 10232

Candidates must be currently enrolled in a graduate degree program in an accredited college, university or law school.

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.