Predictive Analytics Intern - job 1 of 2

As a Predictive Analytics Intern at the Administration for Children’s Services (ACS), you'll support the Predictive Analytics Team by engaging in extensive feature engineering, model building through various methodologies, and data visualization. Your responsibilities will also include performing data modeling, analyzing features for importance, and drafting literature reviews on best practices pertinent to child welfare and juvenile justice.

Candidates interested in the Predictive Analytics Intern position at ACS must be graduate students currently enrolled in an accredited college or university, or who have graduated within a year of the current program year. A strong background in analytics and familiarity with data modeling methodologies will be beneficial.

The Predictive Analytics Internship at ACS plays a crucial role in enhancing child welfare by developing a predictive risk model that identifies high-risk cases requiring timely intervention. This model aims to improve the effectiveness of the child protection services by ensuring that critical cases receive attention and resources quickly.

The Predictive Analytics Internship position at ACS is based in New York City, and while specific remote work policies can vary, it is typically expected that interns will be present in the office to collaborate closely with the team and participate in hands-on learning experiences.

Interns in the Predictive Analytics role at ACS will gain practical experience in building data sets for exploratory analysis, developing and analyzing predictive risk models, and understanding performance metrics evaluation. Interns will also enhance their skills in creating dashboards and documenting analyses, preparing them for future careers in data analytics.

To build a predictive model, I would begin with extensive data collection and feature engineering, identifying relevant predictive factors. Next, I would handle any missing or inconsistent data using appropriate imputation methods. Following this, I would select and evaluate various modeling methodologies to determine the best fit for the data and the problem statement.

To ensure accuracy in my analyses, I prioritize thorough data cleaning and preprocessing. Additionally, I often perform cross-validation on my models to confirm their robustness across different data sets, while continuously monitoring performance metrics throughout the process.

I have experience with several visualization tools, including Tableau and Python libraries like Matplotlib and Seaborn. I find Tableau particularly effective for creating interactive dashboards, which can make data insights more accessible to stakeholders.

I would conduct feature importance analysis by utilizing techniques such as correlation analysis, decision tree-based algorithms, or permutation importance. This represents how essential each feature is to the predictive performance of the model, allowing for informed decisions on feature selection.

In a previous project, I encountered a significant amount of missing data. I assessed the patterns of missingness and opted to use multiple imputation methods to fill in the gaps. This preserved the data integrity and provided a more robust dataset for subsequent analyses.

Fairness and accountability in predictive modeling involve ensuring that models do not introduce or perpetuate biases, especially in sensitive areas like child welfare. I keep in mind the FATML (Fairness, Accountability, and Transparency in Machine Learning) principles, implementing checks and balances to uphold these standards during the model development process.

When faced with conflicting data sources, I first verify the credibility of each source. I analyze the data context and explore the reasons behind the discrepancies. If necessary, I consult with subject matter experts to gain clarity and determine the most accurate representation of the data.

My motivation comes from the potential of predictive analytics to create real change in social services. Helping vulnerable populations through informed decision-making resonates with my values and fuels my passion for using data to improve lives.

To present findings to a non-technical audience, I would prioritize simplicity in visualizations, using clear and relatable analogies. I believe storytelling can make complex data more digestible, so I would aim to communicate the core insights and their implications effectively.

One of the biggest challenges in predictive analytics is ensuring data quality and relevance. Poor quality data can lead to faulty models and misguided decisions, which is why it's essential to implement rigorous data governance practices throughout the analysis process.