Senior Data Scientist & AI Engineer

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Department:

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Position Summary:

1. Apply advanced statistical modeling, machine learning, and predictive analytics methods including time-series forecasting (ARIMA, Prophet, LSTM), survival analysis

(Kaplan-Meier, Cox regression), and optimization techniques to institutional data requiring domain knowledge of admissions, financial aid, enrollment, and student

success data to support university-wide analytical initiatives and operational efficiency. 2. Design, implement, and operationalize automated end-to-end statistical and machine

learning workflows using Python, R, and DataRobot, integrating Snowflake via DataRobot REST APIs and custom Streamlit applications to automate model training,

scoring, validation, monitoring, and controlled production deployment.

3. Architect, build, and deploy production-grade AI agent-based decision-support systems (including model configuration and fine-tuning) that enable faculty and administrators to

query, explore, and interpret governed institutional data using natural language, by developing large language model (LLM)-powered agents and retrieval-augmented generation (RAG) pipelines integrated with Snowflake and enterprise data sources to support university-wide analytics.

4. Integrate, manage and process data from multiple higher-education-related data systems, including Banner (direct or through Cognos), Slate, Common Application (Common App), and Workday, as well as external higher-education datasets including IPEDS and National Student Clearinghouse.

5. Identify, define, and validate analytical attributes, measures, dimensions, and derived metrics within enterprise and external higher-education datasets by designing and maintaining analytically meaningful data models and semantic layers (e.g., fact and dimension structures) that translate raw institutional data into consistent, reporting-ready structures supporting statistical analysis, machine learning, AI development, and institutional planning.

6. Respond to data requests by writing and optimizing complex SQL queries and Snowpark python scripts to extract, join, aggregate, and validate large-scale institutional datasets stored in the Snowflake Data Warehouse, including development of custom, reusable analytical views integrating cross-departmental data.

7. Oversee and perform data extraction, transformation, feature engineering, and validation using SQL, Python, R, and Snowpark to build and maintain scalable data pipelines that prepare structured and unstructured data for modeling and AI applications.

8. Collaborate with Data Governance stakeholders to define analytical requirements and support accurate, governed institutional data; contribute to documentation and validation

of business and technical data definitions using Data Cookbook, ensuring consistency, reproducibility, and compliance within institutional analytics.

9. Certify dashboards and metrics through reviews of the underlying data, mathematical assumptions, transformations, and calculations applied; formally approve dashboards

and ensure appropriate access controls and security for trusted reporting.

10. Design, deploy, and automate interactive dashboards, analytical workflows, and self-service analytics products using Strategy (MicroStrategy) and Snowflake Warehouse

through an iterative development process of gathering stakeholder requirements and incorporating feedback to operationalize institutional metrics and AI-generated insights, and communicate complex analytical findings to support actionable decision-making.

11. Establish and maintain version control and model governance best practices using Git and related tools to manage SQL code, analytical scripts, AI models, dashboards, and

documentation across development and production environments; coordinate analytics and data project workflows using ServiceNow utilizing Agile/SCRUM methodologies to

ensure reproducibility, collaboration, and controlled deployment.

12. Mentor graduate students and junior data scientists, leading technical ideation for strategic projects involving mathematical modeling, machine learning model development, and design of statistical hypothesis tests (e.g., regression-based inference and experimental evaluation), and providing guidance in Python- and SQL-based data analysis, AI engineering, and development of AI-enabled applications.

EDUCATION AND EXPERIENCE REQUIREMENT:

Requires a Ph.D. in Mathematics or Related Field and 1 year of experience in job offered or 1 year of experience in the Related Occupation.

RELATED OCCUPATION:

Data Scientist or any other job title performing the following job duties:

1. Applied advanced statistical modeling, machine learning, and predictive analytics methods including time-series forecasting (ARIMA, Prophet, LSTM), survival analysis (Kaplan-Meier, Cox regression), and optimization techniques to institutional data requiring domain knowledge of admissions, financial aid, enrollment, and student success data to support university-wide analytical initiatives and operational efficiency.

2. Designed, implemented, and operationalized automated end-to-end statistical and machine learning workflows using Python, R, and DataRobot, integrating Snowflake via DataRobot REST APIs and custom Streamlit applications to automate model training, scoring, validation, monitoring, and controlled production deployment.

3. Integrated, managed and processed data from multiple higher-education-related data systems, including Banner (direct or through Cognos), Slate, Common Application (Common App), and Workday, as well as external higher-education datasets including IPEDS and National Student Clearinghouse.

4. Responded to data requests by writing and optimizing complex SQL queries and Snowpark python scripts to extract, join, aggregate, and validate large-scale institutional datasets stored in the Snowflake Data Warehouse, including development of custom, reusable analytical views integrating cross-departmental data.

5. Collaborated with Data Governance stakeholders to define analytical requirements and supported accurate, governed institutional data; contributed to documentation and validation of business and technical data definitions using Data Cookbook, ensured consistency, reproducibility, and compliance within institutional analytics.

6. Assisted the Lead Data Scientist with exploratory artificial intelligence and natural language processing initiatives in higher-education contexts by writing Python- and SQL-based code to test and evaluate large language models (including models accessed through Snowflake Cortex); conducted prompt experiments, reviewed and validated generated outputs against institutional data and use cases, and summarized experimental results to inform future projects as determined by the Lead Data Scientist.

7. Examined student, academic, and operational data using statistical summaries and visualizations to support subsequent modeling, reporting, and analytical inquiries aligned

with strategic institutional priorities; assisted senior team members in designing and creating custom analytical views and data structures.

8. In support of feature engineering and data processing efforts, wrote SQL queries and Python-, R-, and Snowpark-based scripts to extract, clean, and transform data; assisted the Lead Data Scientist by preparing analysis-ready datasets, validating transformations, and troubleshooting data issues during experimentation.

9. Reviewed analytical outputs, dashboards and metrics for accuracy and consistency by verifying calculations, assumptions, and data transformations; assisted senior team members by identifying discrepancies, testing dashboard functionality, and documenting data definitions used in reports and analyses.

10. Built dashboards, reports, tables, and visualizations in MicroStrategy and Snowflake based on requirements, wireframes, and specifications provided by the Director of Data Analytics; prepared analytical outputs to support academic and administrative reporting purposes.

11. Maintained analytical code, SQL queries, and documentation following departmental best practices; followed team standards for organizing scripts, documenting assumptions, and supporting reproducibility of analyses across projects; used ServiceNow to track assigned tasks and follow team workflows.

12. Provided technical guidance and support to student employees on projects developed by the Lead Data Scientist, including mathematical modeling and machine learning projects involving Python- and SQL-based data analysis and model experimentation tasks.

SALARY RANGE:

$90,272.00 to $121,800.00/year

At the university's discretion, the education and experience prerequisites may be exempted where the candidate can demonstrate to the satisfaction of the university an equivalent combination of education and experience specifically preparing the candidate for success in the position.

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NJIT considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as, market and organizational considerations when extending an offer. This pay range represents base pay only and excludes any additional items such as incentives, bonuses, or other items.

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Additional Information:

As an EEO employer NJIT is committed to building a diverse and inclusive teaching, research, and working environment and strongly encourages applications from individuals with disabilities, minorities, veterans, and women.

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