Location: Mumbai, Maharashtra, India

Job Summary 

As an Associate within Internal Audit Data Analytics, you will translate audit objectives into clear analytical questions and hypotheses, pull together complex data from multiple sources, and apply techniques ranging from descriptive analysis to anomaly detection and predictive methods. You will partner closely with audit leads and stakeholders to refine requirements, communicate insights with clear visuals and narratives, and embed analytics into audit scoping, testing, and continuous monitoring. You will also help accelerate the function through automation, streamlining repeatable analysis and testing where appropriate, and through emerging capabilities, leveraging platforms like Databricks and GenAI/AI/ML approaches where they add measurable value, while maintaining strong standards for data quality, governance, and auditability.

Job Responsibilities

• Deliver end-to-end analytics and data science solutions across the audit lifecyclefrom problem framing and requirements through data acquisition, analysis/modeling, visualization, and deploymentusing tools including 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 (descriptive, diagnostic, predictive, and anomaly detection) to support risk-based audit scoping and execution.
• Partner closely with audit leads and key stakeholders to shape and refine the analytics and data science requirements; proactively manage relationships, expectations, scope changes, and communications to drive value and efficiency based results.
• Engineer repeatable, scalable analytics and data science based solutions (datasets, reusable code modules, workflows, dashboards, and templates) that improve efficiency and enable auditor self-service where appropriate.
• Design and develop solutions for non - audit cycle based activities, including continuous auditing, continuous monitoring, automated testing, advanced testing, and event/trigger-based analytics to identify emerging risks.
• Apply strong data management and governance practicesincluding data lineage, data quality assessment, access controls, documentation, and definitions/metadatato ensure analytics are reliable, auditable, and reproducible.
• Implement quality controls for analytic outputs, including validation checks, reasonableness testing, peer review, and clear documentation of assumptions, limitations, and interpretability (especially for advanced models).
• Manage multiple concurrent deliverables by planning work, prioritizing effectively, and meeting timelines and budget expectations while maintaining high standards for accuracy and usability.
• Continuously evaluate and adopt new tools/techniques to improve team effectiveness; recommend enhancements to processes, automation opportunities, and platform capabilities.
• Communicate insights clearly to varied audiences (audit teams, technology/data partners, and senior stakeholders), tailoring messaging and visuals to drive understanding and action.
• Contribute to team knowledge-sharing by providing perspectives on where analytics/data science can add value, and by supporting enablement through guidance, demos, and lightweight training.

Required Qualifications, Capabilities, and Skills:

• Bachelors degree in Computer Science, Data Analytics, Data Science, Information Systems, Engineering, or a related discipline (or equivalent practical experience).
• 3+ years of experience in Audit, Data Analytics, Data Science, Risk/Controls, or a closely related role.
• Demonstrated experience working with large, complex datasets (multiple disparate sources, high volume), performing data wrangling, validation, enrichment and building analytics and data science based solutions..
• Proven, recent track record of building and delivering repeatable, production-ready data science and analytical solutions (e.g., automated workflows, dashboards, anomaly detection, model development)
• Strong understanding of data ecosystems (databases, data warehouses/lakes, ETL/ELT patterns, 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 with the ability to explain technical concepts to non-technical audiences; strong interpersonal skills to build partnerships and influence outcomes.
• Strong critical thinking and structured problem-solving skillsable 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, proactive; demonstrates accountability, sound judgment, and the ability to operate through ambiguity while maintaining high standards. Strong professionalism and integrity; able to work with limited supervision.