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Analyze large and diverse datasets across ARC — including audit findings, ethics and compliance signals, investigation data, and management action items — to uncover risk signals and patterns not visible through standard coverage approaches. Develop and test analytical hypotheses in a rapid, experimental model; iterate on approaches across weeks rather than quarters and document learnings from both successful and unsuccessful analyses. Connect disparate data sources across organizational boundaries to surface systemic risks, emerging trends, and areas that warrant proactive audit attention. Apply a "red team" lens to ARC's risk landscape: scan broadly across data, generate new hypotheses about where risk may be concentrating, and present findings that challenge existing assumptions. Determine appropriate analytical and statistical techniques to address audit and risk research questions; execute analyses and interpret results with clear, actionable recommendations. Build and maintain reusable analytical frameworks, models, and self-service reporting solutions that improve ARC's ongoing visibility into enterprise risk. Evaluate data quality, integrity, and fitness-for-purpose prior to analysis; independently address or escalate data issues in partnership with Data Engineering and other teams. Translate analytical findings into clear risk narratives — presenting insights through dashboards, visualizations, reports, and talking points tailored to audit, compliance, and leadership audiences. Share and simplify complex analyses into accessible summaries that enable decision-makers to act on data-driven recommendations quickly. Work within and across teams — including audit leads, investigation teams, compliance operations, and data engineering — to align data sources, methodologies, analytical tools, and business priorities. Support and advise on the design of formal experiments or analytical frameworks to evaluate the impact of new audit approaches, risk controls, or operational changes. Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR equivalent experience. Exposure to audit, risk management, internal controls, compliance operations, ethics and compliance programs, or financial operations — through prior roles, internships, or coursework. Experience with Python, R, or another scripting/statistical language for analysis, data wrangling, or automation. Experience building dashboards, visualizations, or self-service reporting tools (e.g., Power BI, Tableau, or equivalent). Familiarity with large-scale data platforms or cloud environments (e.g., Azure Synapse, Databricks, Azure Data Lake, or equivalent). Experience identifying patterns or signals in unstructured or cross-domain data; comfort with exploratory data analysis without predefined hypotheses. 1+ year(s) of hands-on experience in data analytics, business intelligence, data science, or a related field — including experience analyzing datasets to answer business or research questions. Proficiency in SQL or equivalent data querying language for extracting, joining, and transforming data from structured sources. Demonstrated ability to interpret analytical outputs and translate findings into clear, audience-appropriate summaries or recommendations.