Data Scientist

Data Scientist role at Association for Institutional Research (AIR)

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• Technical Competence in AI Tools & Frameworks: Deploy generative AI platforms (ChatGPT, Claude, MidJourney, Hugging Face) to enhance data analysis, reporting, and decision-making. Use machine learning frameworks (TensorFlow, PyTorch, scikit-learn) to support applied analytics projects. Integrate AI models via APIs and cloud platforms (AWS, Azure, GCP) to scale institutional solutions. Apply AI techniques such as NLP and generative modeling to analyze unstructured data (e.g., survey comments). Ensure responsible AI use by documenting assumptions, limitations, and risks, and communicate results to non-technical stakeholders. Lead or support workshops promoting best practices in AI and analytics.
  
• Data Management: Use WhereScape and Microsoft SQL Server for data warehouse automation and efficient data management. Generate standardized reports with Cognos. Clean, preprocess, and organize datasets for reporting and modeling. Maintain data integrity through standards, validation, and documentation. Contribute to data initiatives involving metadata, data quality, model design, extraction, dashboard development, analytics tool evaluation, system configuration, and end-user support.
  
• Data Science & Advanced Analytics: Conduct advanced statistical, predictive, and prescriptive analysis using R, Python, and SQL. Develop and validate models (regression, random forests, neural networks) and scale prototypes into production. Apply forecasting and modeling to support academic and strategic planning. Write statistical designs, conduct analyses, and generate predictive insights. Analyze text data using NLP, topic modeling, and sentiment analysis. Run experiments (A/B testing) to evaluate institutional initiatives.
  
• Cross-Functional Collaboration: Partner with offices such as the Registrar’s to promote data-informed decision-making. Collaborate with IT, architects, and analysts to design and manage analytics platforms aligned with strategic priorities. Serve as the data science expert on multi-department projects, guiding end-to-end analytics processes.
  
• Basic Analytics & Reporting: Support operational and strategic initiatives through data analysis projects. Apply statistical methods, querying, scripting, and data modeling to generate reports, dashboards, and visualizations. Conduct exploratory data analysis to identify trends and anomalies. Develop automated workflows to ensure consistent data delivery. Create engaging dashboards that communicate insights to diverse audiences, including campus leadership.
  
• Strategy Development: Contribute to developing and executing data and analytics strategies with campus leadership. Support initiatives involving the enterprise data lake, data warehouse, BI tools, machine learning, and AI. Provide consulting and analytic guidance to align campus projects with institutional goals.
  
• Non-Essential Functions: Responsibilities listed are not exhaustive; additional tasks may be assigned as needed.
  

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• Bachelor’s degree and four (4) years of professional experience in data science or a related data-focused field OR Master’s degree in Analytics, Data Science, Statistics, Computer Science, Information Systems, or a related field and two (2) years of professional experience in data science or a related data-focused field.
  
• Two (2) or more years experience with designing, implementing, and administering data lakes and warehouses, ETL or data warehouse automation solutions, enterprise reporting platforms (Cognos, Business Objects, Microstrategy, or similar), data visualization platforms (Tableau Server/Cloud, Power BI, Qlik, or similar), relational and NoSQL database platforms like Microsoft SQL Server, MySQL, Oracle, MongoDB, and DocumentDB.
  
• Proficiency in SQL and strong programming skills with experience in one or more modern programming languages (Python, R, Java or similar). Experience with data wrangling, cleaning, and preprocessing. Expertise with data visualization tools and best practices. Knowledge of various statistical models (generalized linear models, hierarchical models, nonparametric models, regression trees, random forests) and a solid understanding of descriptive and inferential statistics, probability, and experimental design. Ability to explain model assumptions, limitations, and results to non-technical audiences.
  

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• Familiarity with big data tools (Spark, Hadoop) and cloud environments (AWS, GCP, Azure).
  
• Demonstrated experience with at least one version control tool such as Git, CVS, SVN, or similar.
  

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• This role is based in a typical office setting without any unique physical or environmental requirements.
  

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• Flexible scheduling options determined by role.
  
• Medical, prescription drug, vision, dental, life, and long-term disability insurance options.
  
• 10% employer contribution to your retirement plan (no contribution requirement for non-exempt positions).
  
• Generous paid time off, including vacation and sick time, a community service day, and 19 paid holidays (including two full weeks off for Winter Break).
  
• Full-time and part-time members of the faculty and staff are eligible for tuition remission for themselves. Additionally, full-time members of the faculty and staff are eligible for tuition remission for their spouse/spousal equivalent and are eligible for various tuition programs for their children. Credit for full-time benefits eligible employment at other institutions of higher education will be applied to waiting periods.
  
• A comprehensive employee wellness program including program incentives.
  
• A myriad of other benefits, including parental leave, an employee assistance program, fitness center membership, and the power of your Bucknell ID card.
  

To apply, please click here.