Join our dynamic Audit Analytics team, where you’ll leverage advanced data science and analytics to shape the future of audit. You’ll collaborate with talented colleagues and stakeholders, using innovative tools and techniques to deliver impactful solutions that enhance risk management and business value.
As an Audit Analytics & Data Science Associate within our Asset & Wealth Management Audit Team, you will deliver comprehensive analytics and data science solutions across the audit lifecycle. You’ll partner with audit leads and stakeholders to translate objectives into actionable insights, engineer scalable solutions, and communicate findings that drive informed decision-making. Your work will help strengthen risk management and support continuous improvement in audit processes.
Job Responsibilities
- Deliver end-to-end analytics and data science solutions across the audit lifecycle, from problem framing and requirements through data acquisition, analysis/modeling, visualization, and deployment using tools such as SQL, Python, Alteryx, Databricks, Tableau, Agentic Studio, Smart SDK, and related platforms
- Translate audit objectives into clear analytic hypotheses and test designs, selecting appropriate methods to support risk-based audit scoping and execution
- Partner closely with audit leads and stakeholders to shape and refine analytics and data science requirements, proactively managing relationships, expectations, and communications
- Engineer repeatable, scalable analytics and data science solutions—including datasets, reusable code modules, workflows, dashboards, and templates—to improve efficiency and enable auditor self-service
- Design and develop solutions for non-audit cycle activities, including continuous auditing, continuous monitoring, automated testing, advanced testing, and event/trigger-based analytics
- Apply strong data management and governance practices to ensure analytics are reliable, auditable, and reproducible
- Implement quality controls for analytic outputs, including validation checks, reasonableness testing, peer review, and clear documentation
- Manage multiple concurrent deliverables by planning work, prioritizing effectively, and meeting timelines and budget expectations
- Continuously evaluate and adopt new tools and techniques to improve team effectiveness and recommend process enhancements
- Communicate insights clearly to varied audiences, tailoring messaging and visuals to drive understanding and action
- Contribute to team knowledge-sharing by providing perspectives on analytics and data science value, and supporting enablement through guidance, demos, and training
Required Qualifications, Capabilities, and Skills
- Bachelor’s degree in Computer Science, Data Analytics, Data Science, Information Systems, Engineering, or a related discipline (or equivalent practical experience)
- Minimum 3 years of experience in Audit, Data Analytics, Data Science, Risk/Controls, or a closely related role
- Demonstrated experience working with large, complex datasets, performing data wrangling, validation, enrichment, and building analytics and data science solutions
- Proven track record of building and delivering repeatable, production-ready data science and analytical solutions such as automated workflows, dashboards, anomaly detection, or model development
- Strong understanding of data ecosystems, including databases, data warehouses/lakes, ETL/ELT patterns, and APIs/files, and how technology design influences risk, controls, and auditability
- Working knowledge of technology and data risks/controls and the ability to apply this experience when designing solutions
- Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical audiences and build partnerships
- Strong critical thinking and structured problem-solving skills, able to frame ambiguous questions, test hypotheses, and identify practical solutions under time constraints
- Ability to manage and deliver multiple concurrent tasks with attention to detail, effective prioritization, and follow-through against timelines
- Working knowledge of data management principles such as data quality, lineage, metadata, governance, privacy/access considerations, and documentation practices that support reproducibility
- Self-motivated and proactive, demonstrating accountability, sound judgment, and the ability to operate through ambiguity while maintaining high standards
Preferred Qualifications, Capabilities, and Skills
- Experience with workflow enablement and agentic tooling, such as Agentic Studio, Smart SDK, or AI code assistance tools (Claude Code, GitHub Copilot)
- Experience with cloud data platforms such as Databricks or Snowflake
- Experience with visualization analytics tools such as Tableau or similar platforms