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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Reporting and Analytics - **Company:** Northwestern University - **Location:** Evanston, IL, United States - **Experience:** Experienced - **Salary:** $89,500.0 - $93,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Information Engineering, Data Visualization, IBM Cognos Business Intelligence, Python (Programming Language), Oracle (Applications), Cloud Services, Azure Machine Learning, Salesforce.Com, SQL Databases, Tableau (Software), Data Processing, Feature Engineering, Large Language Models, Information Technology, Data Analytics, Data Pipelines - **Published:** August 29, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18134128?backUrl=%2Fcareer%2F18134128%2FData-Scientist-Reporting-Analytics-Illinois-Evanston ## About the Role * BACHELOR'S DEGREE IN DATA SCIENCE, STATISTICS, COMPUTER SCIENCE, OR RELATED FIELD * 3-5 years experience in data science, advanced analytics, or quantitative roles * Strong proficiency in SQL (Oracle preferred) and Python (modeling, data manipulation) * Experience with building, deploying, and maintaining predictive models in a production environment * Experience with reporting and data visualization tools such as Tableau and Cognos * Ability to translate complex analysis into actionable business insights * Experience in fundraising, nonprofit, or higher education advancement analytics Preferred Qualifications (Education, Experience, Certifications, Skills) * Experience with Salesforce (including CRM Analytics) or similar CRM platforms * Experience with Azure / Azure ML or similar cloud services platforms * Experience with AI/LLM applications ## Description The Data Scientist plays a critical role in enabling data-driven fundraising and engagement strategies across the Alumni Relations and Development organization. This role focuses on advanced analytics, predictive modeling, and data engineering to generate actionable insights and deliver scalable data science solutions within the advancement ecosystem. The position requires the ability to understand stakeholder questions and business needs and translate them into scalable, data-driven solutions that drive measurable outcomes through descriptive analytics, predictive modeling, and advanced data science methods. This is a hands-on, mid-level role suited for someone who can both develop robust models and operationalize them, partnering closely with fundraising and operations teams to turn data into meaningful impact. The ideal candidate brings strong analytical and problem-solving skills, excels at stakeholder communication and storytelling, demonstrates a natural curiosity for exploring data, and can effectively translate complex models into clear, actionable insights for stakeholders without a data science background. This role sits within the Reporting & Analytics function and supports the broader organization. The team is focused on advancing the organization's ability to scale impact through the effective use of data, modern analytics, and integrated technology. Please note: This role is eligible for a hybrid work arrangement, up to two days remote and three onsite. *Note: Not all aspects of the job are covered by this job description. Specific Responsibilities: * Develop and deploy predictive models to support various fundraising, campaign, and operational initiatives, including donor propensity, capacity, engagement, and retention. * Partner with senior leadership and various teams to identify opportunities where data science can drive impact. * Design and implement data pipelines and feature engineering processes to support model development and operationalization. * Translate complex analytical outputs into clear, compelling insights and recommendations for non-technical stakeholders. * Collaborate with reporting and central IT teams to integrate model outputs into our CRM, dashboards, and decision-making workflows. * Ensure data quality, integrity, and governance standards are upheld across all analytical work. * Monitor model performance and continuously refine approaches based on new data and evolving business needs. * Contribute to building a scalable and sustainable analytics ecosystem within the organization. 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