Project Coordinator/Manager of Data and Statistics Engineer

at  Labor Department of

MINIMUM QUALIFICATIONS

Bachelor’s degree and five years of specialized experience. Two years of the experience must have been at a supervisory level.

Substitutions:

• J.D. or master’s degree and four years of specialized experience.
• Ph.D. and three years of specialized experience.

PREFERRED QUALIFICATION: Ability to initiate and maintain relationships across a diverse set of stakeholders.

As a Project Coordinator/Manager of Data and Statistics Engineer within the Research and Statistics Bureau, the incumbent will perform the following duties but are not limited to the following:

• Demonstrate the ability to work in a multidisciplinary environment, helping researchers with data integration and management and developing pipelines to empower data analysis throughout scientific and translational research.
• Responsible for managing and training other staff in the Bureau in areas of subject matter expertise.
• Designing and executing analysis independently and collaboratively with other research scientists and subject matter experts, including interacting with other research/data scientists in NYSDOL.
• Presenting and publishing plans, processes, or findings at conferences/journals.
• Collaborate with economists, analysts, and others working in this area to identify relevant public and confidential data sources (labor market data, education data, etc.).
• Develop data pipelines to extract data from various sources, ensuring secure access to confidential data.
• Clean and pre-process the data to handle inconsistencies, missing values, formatting issues, etc.
• Design and implement a secure data storage system to handle large volumes of data from diverse sources.
• Build and maintain data warehouses and lakes for efficient data retrieval and analysis. Ensure data security and access control, especially for confidential data.
• Work with economists, analysts, and others to define research questions and identify relevant data points.
• Develop statistical models and machine learning algorithms to analyze essential trends, relationships, and patterns in the data.
• Conduct exploratory data analysis (EDA) to understand the data’s characteristics and potential biases.
• Collaborate with economists, analysts, and others working in this area to create non-technical informative dashboards, reports, and visualizations for public consumption.
• Design benchmarks and metrics relevant to the economic research being conducted. Explore and implement AI techniques, such as natural language processing (NLP), to analyze textual data within the system.
• Develop machine learning models for forecasting, anomaly detection, or data segmentation on approved datasets. Ensure responsible AI practices by mitigating bias and ensuring model explainability.