Manager, Data Science and Machine Learning, Audit and Assurance

Overview

In this role, you will lead data science and analytics delivery within Deloitte’s Audit & Assurance practice, shaping insights that help clients understand risks and key drivers. You’ll work closely with cross-functional teams to develop ML tools and data solutions, enabling more transparent reporting and better decision-making. You will manage multiple analytics projects across industries, applying advanced techniques in Python, ML, and cloud platforms. This position offers the chance to influence how assurance uses data to build trust and resilience for clients.

Pay / Benefits

• hybrid working
• wellbeing support
• world-class development
• purpose-driven culture
• diverse and inclusive environment
• opportunity to work with leading clients

Responsibilities

• Deliver analytics and data science services across audit and assurance offerings to extract meaningful client insights
• Develop and deploy innovative data science and machine learning tools to support evolving audit needs
• Support clients in large-scale data handling, manipulation, analysis, and modelling
• Collaborate effectively within diverse, inclusive teams and contribute to a culture that recognises contributions

Key requirements

• Strong problem-solving skills with ability to generate original solutions
• Experience coaching junior data scientists/analysts
• Experience reviewing code and documentation to a high standard
• Proficiency in Python (pandas, numpy, scikit-learn)
• End-to-end management of multiple data science/analytics projects across industries
• DevOps project management experience
• Cloud experience (Azure, AWS)
• Git experience
• Solid grounding in mathematics, probability, statistics
• Deep knowledge of ML techniques (supervised/unsupervised)
• Understanding of Large Language Models and Generative AI frameworks (prompt engineering, fine-tuning)
• Excellent communication and data presentation, able to persuade non-technical audiences
• Self-driven, independent yet collaborative
• Able to apply data science principles through a business lens

• Strong communication
• Collaboration
• Self-motivation
• Python (pandas, numpy, scikit-learn)
• R (desirable)
• Azure/AWS cloud environments

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