Senior Data Analyst - School of Medicine

Brigham Young University•Provo, UT

1d•Onsite

About The Position

The Senior Data Analyst leads complex analytic projects and communicates actionable insights related to the School of Medicine’s mission, strategic priorities, educational effectiveness, accreditation expectations, student performance, and program outcomes. This position develops analytic methods, descriptive and predictive statistical models, reports, and dashboards, that help leaders monitor outcomes, identify risks and opportunities, and support continuous improvement across the School of Medicine. This position is critical to the success of the BYU School of Medicine, a newly established organization within the university. As the school continues to launch and grow, a successful candidate must demonstrate adaptability and a willingness to take on evolving responsibilities and assignments to support its effective implementation and ongoing operations. The School of Medicine is looking for someone who is mission-aligned and will support the vision to foster a spiritually enriching environment where employees and students can deepen their testimonies of Jesus Christ while preparing students for a lifetime of service as Christ-centered physicians.

Requirements

A firm commitment to the mission of BYU.
Bachelor’s degree in statistics, data analytics, institutional research, education, psychology, public health, computer science, or related field; or equivalent education and experience.
5 years of analyst experience.
Experience analyzing data and preparing reports, briefs, dashboards, visualizations, or summaries for decision-making.
Experience using quantitative methods to identify trends, patterns, comparisons, benchmarks, or performance indicators.
Experience communicating data findings clearly to technical and non-technical audiences.
Experience working with sensitive or confidential data and applying appropriate standards for access, use, reporting, and confidentiality.
Experience developing and validating predictive models, classification models, forecasting models
Advanced data analysis, data interpretation, and data visualization skills.
Working knowledge of Microsoft Power BI or comparable reporting/analytics tools.
Ability to develop analytic methods, statistical models, visualizations, reports, and summaries.
Ability to develop, evaluate, and interpret predictive models and risk indicators and communicate model findings, assumptions, limitations, and appropriate uses to decisionmakers.
Ability to interpret complex data and present and explain findings, implications, limitations, and recommendations to technical and non-technical audiences.
Ability to work collaboratively with technical and non-technical stakeholders.
Ability to apply approved data definitions, reporting standards, confidentiality expectations, and appropriate-use guidance.
Strong written and verbal communication skills.
Sound judgment and discretion when working with student, faculty, assessment, evaluation, accreditation, and institutional data.
Demonstrated ability to independently learn, evaluate, and apply emerging technologies, analytics methods, AI capabilities, and data platforms in a rapidly changing environment.
Demonstrated curiosity, creativity, adaptability, and commitment to continuous professional growth and innovation.
Familiarity with AI-enabled analytics tools and responsible-use practices for accurate, explainable, confidential, and secure analysis.

Nice To Haves

Master’s degree in statistics, data analytics, institutional research, educational research, public health, psychology, data science, health professions education, or related field.
Experience in higher education, medical education, health professions education, institutional research, program evaluation, educational assessment, student success analytics, or curriculum analytics.
Experience with Power BI, Tableau, Excel, SQL, R, Python, SPSS, SAS, or comparable analytic/reporting tools.
Experience analyzing and presenting student performance data, assessment data, survey data, course/clerkship outcomes, program outcomes, strategic indicators, or accreditation-related evidence.
Experience with AI-enabled tools for analysis, visualization, summarization, or documentation.
Experience developing early-alert systems, or other analytics used to identify educational performance, progression, retention, or outcome risks.
Advanced skills in Power BI, DAX, Power Query, SQL, R, Python, statistical modeling, or comparable tools.
Knowledge of educational outcomes, assessment data, student progression data, course/clerkship performance data, survey data, curriculum data, CQI reporting, or accreditation monitoring.
Ability to support stakeholders in trusted self-service analysis through user-friendly reports, dashboards, documentation, and interpretation guidance.
Advanced skills in predictive analytics, statistical modeling, machine learning techniques appropriate for structured educational data, and validation of analytic models using R, Python, or comparable tools.
Relevant training or certification in data analytics, statistics, institutional research, business intelligence, Power BI, data visualization, or related areas.

Responsibilities

Analyze evidence related to educational effectiveness, accreditation expectations, strategic priorities, and mission fulfillment.
Analyze performance indicators, benchmarks, trends, and comparative data that support committee and leadership decision-making.
Identify strengths, gaps, risks, and opportunities for improvement.
Support accreditation monitoring and CQI activities through clear summaries of relevant data, trends, and findings.
Analyze the effectiveness of actions taken to advance strategic priorities, improve the educational program, mitigate accreditation risks, or strengthen student outcomes, using appropriate comparison, trend, and outcome-analysis methods.
Analyze aggregate student performance, assessment, and progression data to support monitoring of student achievement and educational effectiveness.
Develop and evaluate predictive models, risk indicators, and early-alert analyses that help identify trends, risk factors, emerging patterns, and students or groups at risk of not meeting performance or progression expectations.
Translate student performance findings into actionable evidence that informs curriculum evaluation, student support, accreditation monitoring, and continuous improvement efforts.
Collaborate with appropriate stakeholders to ensure analyses are accurate, appropriately aggregated, and interpreted in context.
Develop committee-ready visualizations, reports, dashboards, slide presentations, executive summaries, and briefing materials that support leadership and decision-making groups.
Communicate and present findings clearly to technical and non-technical audiences.
Lead and maintain recurring analyses and reporting products that support committees, curricular review, accreditation monitoring, strategic planning, and leadership review.
Ensure reports include appropriate definitions, context, limitations, and interpretation guidance.
Translate analytic findings into actionable insights that support informed decision-making and continuous improvement.
Support stakeholder interpretation of findings by explaining analytic methods, assumptions, limitations, and implications for decision-making.
Serve as the analytic subject-matter expert for assigned projects, advising stakeholders on methods, model performance, findings, assumptions, limitations, implications, and appropriate uses of evidence.
Develop analytic methods and descriptive and predictive models that support reliable interpretation of educational data.
Enable trusted self-service analysis through approved dashboards, reports, data products, and analytic tools that align with approved metric definitions, data structures, and security requirements.
Use AI-enabled analytics tools appropriately and provide guidance that promotes accurate, explainable, confidential, secure, and responsible use of those tools.
Collaborate with school and institutional stakeholders to support effective use of evidence, analysis, reporting, decision-making, and continuous improvement efforts.
Apply approved standards for confidentiality, role-based access, de-identification, data use, and reporting.
Support documentation of data-to-decision and decision-to-action workflows.
Contribute to ongoing improvement of analytic methods, reporting processes, dashboard adoption, data quality, and stakeholder data literacy.
Demonstrate continuous learning, adaptability, and curiosity by evaluating emerging technologies, analytics methods, and AI capabilities and identifying opportunities to strengthen analytics services and decision support.
Champion a culture of continuous learning and improvement by supporting initiatives that optimize service delivery, strengthen data-driven decision making, and improve reporting processes.

Benefits

401k. BYU automatically contributes 8% at no cost to you. Additionally, if you contribute 5%, BYU adds an additional 4% (Rehires may qualify for different retirement plans)
Excellent work-life balance: 13 paid holidays + 22 days paid vacation + 12 sick days, accrued annually
Employee assistance program, available to the employee and all members of their household
Tuition benefits for employees and eligible family members
Access to athletic facilities
Excellent medical/dental benefits
Short/long-term disability benefits
Paid parental and maternity leave
Wellness Program
Free on-campus parking
Free UTA passes for employees, spouses, and qualified dependents
Discounts at the BYU Store and for many events at BYU