Business Data Scientist

The Business Data Scientist will transform raw data into actionable insights and forward-looking predictions that drive enrollment growth, optimize marketing spend, improve student outcomes, and guide strategic decision-making for this high-growth initiative. This position requires both strong analytical capabilities and advanced data science expertise, including the ability to build predictive models and machine learning algorithms, combined with the business acumen to translate complex findings into clear recommendations for non-technical stakeholders.

Reporting to the Executive Director of Operations, this position goes beyond describing what happened - it predicts what comes next. The ideal candidate designs and deploys models that identify at-risk students, forecast enrollment trends, and optimize decisions before problems arise, using data from across the organization including CRM tools, operational systems, marketing platforms, and financial systems.

Syracuse University is building something new. We're launching SU Global to reimagine how we support and scale accessible online pathways for non-traditional learners, in a dynamic, innovative, and data-driven environment. That means rethinking how we work.

This position requires regular on-campus presence and occasional schedule flexibility, including evenings and weekends based on student and operational needs. Staff operate in a fast-paced, collaborative environment supporting non-traditional learners through an evolving, data-informed model.

We're looking for team members who thrive in:

* High-energy, in-person environments where innovation happens face-to-face

* Flexible scheduling that follows student needs, not the clock

* Startup intensity within a world-class university structure

We're not looking for people who want a job. We're looking for builders who want a mission.

Education and Experience

* Bachelor's degree required in data science, statistics, machine learning, mathematics, computer science, information systems, or a related quantitative field.

* Master's degree or PhD strongly preferred.

* Minimum 3-5 years of experience in data science, advanced analytics, or machine learning roles in high-growth organizations.

* Demonstrated experience building and deploying predictive models, machine learning algorithms, and statistical models that produce actionable operational outcomes - not just reports.

* Experience applying data science skills across large, complex datasets in any industry or domain is valued.

* Experience managing multiple concurrent projects in fast-paced environments.

Skills and Knowledge

Data Science & Machine Learning (Required)

* Proficiency in Python or R for data science, statistical modeling, and machine learning

* Experience building supervised and unsupervised models: classification, regression, clustering, survival analysis

* Hands-on experience with predictive modeling frameworks (scikit-learn, XGBoost, or equivalent)

* Understanding of causal inference, A/B testing, and experimental design

* Ability to validate, tune, and communicate model performance metrics (AUC, precision/recall, RMSE, etc.)

Technical Proficiency

* Advanced SQL for data extraction, transformation, and manipulation

* Advanced Excel including pivot tables, formulas, and statistical functions

* Expert proficiency with Tableau and/or Power BI for visualization and dashboard development

* Familiarity with CRM systems, student or customer information systems, and marketing analytics platforms; experience with enterprise SIS or ERP platforms a plus

* Understanding of web analytics, survey tools, and data warehousing concepts

Analytical & Problem-Solving

* Highly analytical mindset to identify patterns, trends, and causal relationships in complex datasets

* Strategic thinking to connect predictive insights to business strategy and operational action

* Critical thinking to evaluate data quality, select appropriate analytical approaches, and communicate model limitations honestly

Communication & Collaboration

* Excellent communication skills to explain predictive models and complex concepts to non-technical audiences

* Strong data storytelling capabilities - translating model outputs into narratives stakeholders can act on

* Collaborative work style with demonstrated ability to build relationships across organizational boundaries

Domain Adaptability & Context

* Demonstrated ability to apply data science skills across industries or data domains; no specific sector experience required

* Comfort working within complex, multi-stakeholder organizations where data informs decisions across multiple functions

* Willingness to quickly learn domain-specific context, including student lifecycle, enrollment funnels, and operational workflows, on the job

* Knowledge of FERPA compliance and ethical data practices a plus; experience with data governance frameworks in any regulated industry is equally relevant

Responsibilities

Predictive Modeling & Data Science

* Design, build, and deploy predictive models and…