Predictive Analytics Intern

As a Predictive Analytics Intern at the Administration for Children’s Services, you will be engaging in various responsibilities, which include extensive feature engineering, utilizing different imputation methods to handle data inconsistencies, and employing various model-building methodologies. Beyond that, you will also perform critical tasks such as forecasting, data pattern analysis, and creating visual data reports using tools like Tableau, R, or Python. The role allows you to contribute to the vital work ACS does in child welfare, analyzing data for high-risk cases.

To qualify for the Predictive Analytics Internship at ACS, candidates must be currently enrolled in a graduate degree program at an accredited institution or have graduated within the past year. This ensures that you have the necessary academic background and skills for the responsibilities involved in the internship. Candidates should ideally have a strong foundation in data analysis and modeling techniques.

During your internship as a Predictive Analytics Intern at the Administration for Children’s Services, you will gain a wide range of skills, from building predictive risk models to analyzing features for importance in the context of child welfare. You'll also learn how to create visual dashboards for data presentations and develop crucial analytical skills to evaluate and define performance metrics, enhancing both your practical and theoretical understanding of predictive analytics.

The Predictive Analytics Team at the Administration for Children’s Services plays a critical role in child welfare by building risk models that help identify cases that require immediate attention. By employing various data analysis methodologies and research practices, the team ensures that cases with the highest risk factors are prioritized, allowing ACS to intervene effectively and provide the necessary support to families and children in need.

To apply for the Predictive Analytics Internship at the Administration for Children’s Services, interested candidates should submit their resume directly through the New York City jobs website, searching for Job ID#707180. It's important to follow the guidelines provided: no phone calls, faxes, or personal inquiries are permitted. Only candidates considered for the position will be contacted, so make sure your application stands out!

When answering this question, focus on specific data modeling techniques you've employed in previous projects or coursework. Describe how you identified which technique was appropriate for the data at hand, and provide examples of the outcomes that resulted from your modeling approach.

Mention any software tools you are proficient in, such as Tableau, Python, R, or any other analytics platforms. Highlight specific projects where you've utilized these tools, explaining how they contributed to your insights or model development.

Emphasize your methodology for identifying and creating features that enhance the predictive power of models. Discuss examples of how you've tackled missing data or inconsistencies in the past and how it influenced your model outcomes.

Provide a detailed example of a situation where you confronted missing data. Discuss the imputation methods you utilized to deal with it and the overall effect on your analysis or predictive accuracy.

Express your passion for social services and how your educational background has prepared you for this field. Discuss any personal experiences that have shaped your interest in child welfare and your desire to make a meaningful impact.

Share your perspective on teamwork in analytics projects. Discuss your experiences collaborating with cross-functional teams and how various perspectives can enrich the data analysis and outcomes, especially in a field as impactful as child welfare.

Discuss your understanding of FATML (Fairness, Accountability, and Transparency in Machine Learning) and its relevance to predictive modeling in child welfare. Provide thoughts on how these principles ensure that models serve communities ethically and effectively.

Outline the performance metrics you typically use to assess predictive models. Discuss how you define success and any specific examples where your evaluation led to model improvement.

Highlight your methods for staying updated on industry trends, such as reading academic journals, following influential figures in analytics, participating in webinars, or engaging with online communities. This shows your commitment to continuous learning.

Identify and discuss potential challenges such as data privacy concerns, bias in data sources, or the complexities of accurately capturing the nuances of social behavior. Discuss how you would proactively address these challenges in your modeling efforts.