Deloitte announced job vacancy for the post of Assistant – Valuation and Analytics Advisory. The place of posting will be at Bangalore & Gurgaon. Candidates who have completed Bachelor’s degree in computer science, Data Science, Information Systems, Statistics, or a related field with 0 – 1 years’ experience with AI/ML tools – e.g., Python (scikit-learn, pandas, PyTorch/TensorFlow), cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI), or similar are eligible to apply. More details about qualifications, job description and roles & responsibilities are as follows
Company Overview
| Name of the Company | Deloitte |
| Required Qualifications | Graduate |
| Skills | Python (scikit-learn, pandas, PyTorch / TensorFlow), cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) |
| Category | Valuation and Analytics Advisory |
| Work Type | Onsite |
Join Deloitte’s Audit & Assurance Valuation and Analytics team to support audit objectives through financial instrument pricing, valuation analysis, and data analytics. In this role, you will work with market data, valuation tools, and audit teams across industries to help assess complex instruments and document technical analyses. This opportunity offers exposure to valuation methodologies, structured products, and cross-functional collaboration in a dynamic professional environment.
Job Details
Θ Positions: Assistant – Valuation and Analytics Advisory
Θ Job Location: Bangalore & Gurgaon
Θ Salary: As per company standards
Θ Job Type: Full Time – Fixed Term
Θ Requisition ID: 364049
Roles and Responsibilities:
- AI Risk, Governance & Controls Assessment
- Support engagement teams in identifying and documenting how client entities are using AI – across financial reporting processes and broader enterprise functions – including intelligent automation, AI agents, and agentic systems.
- Assist in evaluating AI model complexity, reliability, and performance – applying statistical and quantitative methods to assess model inputs, outputs, and assumptions, including drift and year-over-year change, under the direction of senior team members.
- Help assess governance structures and risk management practices around AI systems, referencing recognized frameworks (e.g., NIST AI RMF, ISO/IEC 42001) as part of broader team assessments.
- Support the preparation of assessment reports, maturity scorecards, and other client deliverables that document governance gaps and recommended remediation.
- Audit Response & Controls Testing
- Support the evaluation of process vs. control determinations, including the appropriateness of human oversight and monitoring controls over AI systems (e.g., human-in-the-loop reviews, KPI monitoring controls).
- Assist in designing audit responses to AI-related risks, including controls testing over AI governance, data inputs, and model outputs.
- Support planning and execution of substantive procedures over account balances and transactions impacted by the use of AI (e.g., accounting estimates derived from ML models, automated journal entries, AI-driven reconciliations).
- Communication & Team Collaboration
- Support day-to-day communication with client working teams (e.g., IT, risk, business process owners) as part of the broader engagement team.
- Assist in preparing materials and supporting analysis for status updates and workshops with client stakeholders.
- Communicate technical AI/ML concepts clearly in team settings and client working sessions, under the guidance of senior team members.
- Stay current on developments across the AI landscape and contribute to internal training materials and team knowledge-sharing.
Required Skills & Qualifications:
- 0 – 1 year experience.
- Bachelor’s degree in computer science, Data Science, Information Systems, Statistics, or a related field.
- Hands-on experience with AI/ML tools – e.g., Python (scikit-learn, pandas, PyTorch/TensorFlow), cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI), or similar.
Preferred:
- Foundational knowledge of machine learning, generative AI, LLMs, and agentic/RAG-based systems – how they’re built, validated, monitored, and where they can fail.
- Basic understanding of internal controls frameworks (e.g., COSO, ICFR) and risk assessment principles.
- Exposure to model evaluation concepts, such as assessing model accuracy, robustness, and fitness for purpose.
- Solid grounding in statistics and quantitative methods – including regression, hypothesis testing, and probability.
- Familiarity with the AI regulatory landscape – including the EU AI Act, NIST AI RMF, and related standards.
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