Senior Analyst, Business Data
NIGHTINGALE EDUCATION LLC SOLE MB Salt Lake City, Utah, United States
Higher Education · 1,001-5,000 employees
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About the role
The Senior Data Analyst designs and executes predictive and prescriptive models to inform strategic business decisions. They are responsible for the full analytical lifecycle, including metric definition, data quality governance, and delivering actionable insights to leadership.
What they look for
Requirements
Candidates must have a bachelor's degree in a quantitative field and at least 5 years of experience in predictive or prescriptive analytics. Proficiency in SQL, Python or R, and experience with cloud-based data warehouses and visualization tools are required.
Full description
Job Details: Job Location: OSC Utah - Salt Lake City, UT, Salary Range: Undisclosed, The Senior Data Analyst designs and delivers predictive, prescriptive, and diagnostic analyses that inform strategic business decisions. This role owns the full analytical lifecycle — from hypothesis formation and modeling to executive insight delivery and outcome measurement — and serves as the authoritative source for metric definitions and data-driven recommendations across the organization. The ideal candidate is a self-directed analytical thinker who thrives in ambiguous environments, translates complex findings into actionable recommendations, and sets the analytical standard for the team and enterprise. Key Responsibilities: Analysis & Insight Delivery Predictive & Prescriptive Modeling: Design, build, and validate statistical and machine learning models to forecast outcomes, identify risk, and generate recommendations. Own the full model lifecycle. Hypothesis-Driven Analysis: Frame business problems as testable hypotheses. Design and execute analytical frameworks including A/B tests, causal inference, and regression analysis to generate rigorous conclusions. Analytical Recommendations: Translate findings into insight-driven recommendations for leadership. Recommendations must be tied to measurable business outcomes and supported by documented methodology. Metric Strategy & Governance Metric Definition: Assist with KPI and metric definitions across the organization. Partner with business analysts to define metrics that are measurable, meaningful, and methodologically sound. Lead validation, refinement, and standardization over time. Data Quality & Integrity: Own data quality for all analytical outputs. Implement monitoring frameworks, validation processes, and root-cause resolution. Establish and maintain quality standards. Governance Compliance: Ensure all analytical outputs adhere to data governance standards. Promote consistency in metric definitions, methodology, and reporting. Serve as governance checkpoint before artifacts reach leadership. Partnership & Enablement Analytical Advisory: Serve as the go-to source for analytical interpretation. Ensure analyses are rigorous, conclusions defensible, and recommendations actionable. Push back constructively when requests lack needed rigor. Methodology Documentation: Document analytical methodologies, assumptions, model logic, and limitations to ensure reproducibility, auditability, and knowledge transfer. Training & Capability Building: Elevate analytical capabilities of business users through training, guidance, and tool adoption support. Analytical Project Ownership: Lead analytics-focused projects end-to-end: hypothesis and methodology design, implementation, and impact measurement. Scope analytical work, define success criteria, manage timelines and cross-team dependencies. Qualifications: Education: Bachelor’s degree in Statistics, Computer Science, Data Science, Mathematics, or related quantitative field; advanced degree (MS/PhD) strongly preferred. Experience: 5+ years designing and delivering predictive or prescriptive analytics in cross-functional business environments. Track record of translating analytical findings into measurable business impact — driving decisions and outcomes, not just reporting. Experience with statistical experimentation (A/B testing, causal inference) and hypothesis-driven problem-solving. Technical Skills: Advanced SQL proficiency and experience with cloud-based data warehouses (Snowflake, Redshift, BigQuery). Proficiency in Python or R for statistical modeling and data manipulation. Experience with libraries such as scikit-learn, statsmodels, or equivalent. Familiarity with data transformation tools and practices (e.g., dbt or similar ELT frameworks). Proficiency in visualization platforms (Tableau, Power BI) with ability to build analytical dashboards that drive decisions.