Executive Data Scientist Experimentation Jobs in Syracuse, NY

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.

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, and 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 machine learning algorithms that generate forward-looking insights — not just retrospective reporting. Develop models that identify which current students are at-risk for non-persistence based on causal variables correlated with retention. Build enrollment forecasting models that project future trends, conversion probabilities, and revenue scenarios with quantified confidence levels. Create segmentation and clustering models to identify distinct student populations, behavioral patterns, and intervention targets. Develop attribution and causal inference models to measure the true impact of marketing campaigns, interventions, and program changes. Partner with enrollment, student success, and marketing teams to deploy models in operational workflows, ensuring predictions drive real-time decisions. Document model methodology, assumptions, validation approaches, and performance metrics to ensure reproducibility, transparency, and compliance with FERPA and institutional data governance standards. Descriptive Analytics & Strategic Insights Aggregate and synthesize data from multiple sources — including enterprise data systems, CRM, LMS, marketing automation tools, web analytics, and data warehouses — to identify trends, anomalies, opportunities, and risks. Conduct cohort analyses, funnel analyses, segmentation studies, and comparative assessments to understand program performance, student behavior, and competitive positioning. Translate analytical findings into clear narratives that connect data insights to business implications and recommended actions for executives, program directors, enrollment teams, and operational staff. Provide evidence-based recommendations that are grounded in both descriptive analysis and predictive science. Performance Measurement, KPIs & Dashboards Establish and maintain KPIs aligned with SU Global strategic objectives including enrollment targets, conversion metrics, student success measures, financial performance, and operational efficiency. Develop comprehensive dashboards and automated reporting systems using Tableau, Power BI, or similar tools that provide real-time visibility into critical metrics. Integrate model outputs into dashboards so stakeholders can act on predictions, not just historical data. Monitor data quality and integrity across source systems, identifying and resolving discrepancies and establishing validation protocols. Build self-service reporting capabilities that empower teams to access relevant data independently while maintaining governance and consistency. Cross-Functional Collaboration & Process Improvement Work collaboratively across SU Global functions including enrollment, marketing, student success, finance, operations, and academic programs to understand data needs, align on priorities, and deliver analytical and predictive support. Partner with University central offices including Institutional Research, Registrar's Office, Enterprise Analytics, Enrollment Management, and IT to leverage existing data resources and ensure compliance with governance standards. Identify opportunities to enhance data collection, improve system integrations, automate manual processes, and build scalable data science infrastructure as SU Global grows. Lead meetings to review performance trends, model results, and analytical findings to build organizational capacity for evidence-based, predictive decision-making. Other Duties as Assigned Support special projects, ad hoc analyses, and emerging priorities as SU Global scales and evolves.