
Manager, Data Science and Machine Learning, Audit and Assurance
- London
- Permanent
- Full-time
- Providing data analytics/data science services to deliver meaningful insights to our clients and help them to understand the risks and key drivers for their business through the use of software such as Python, R, Azure, Databricks/other ML services, SQL, Tableau and Power BI.
- Development and delivery of new and innovative data science and machine learning tools and solutions to support evolving audit and assurance needs.
- Helping the team to support our clients in all areas of large data handling, manipulation, analysis, and modelling.
- Working effectively in diverse teams within an inclusive team culture where people are recognized for their contribution.
- Strong problem-solving skills, and capable of generating original solutions to real-world problems.
- Experience of coaching junior data scientists/analysts.
- Experience in reviewing code and documentation to a high standard.
- Experience in using Python (pandas, numpy, scikit-learn).
- End-to-end experience of managing multiple data science and analytics projects in different industries and with different types of data (text, numerical, categorical).
- Experience in project management experience in a DevOps environment.
- Experience in using cloud environment (e.g. Azure, AWS).
- Experience using Git.
- Solid understanding of mathematics, probability, and statistics.
- Deep knowledge in a range of machine learning techniques (Supervised and unsupervised).
- Understanding of Large Language Models, Generative AI frameworks, prompt engineering, fine tuning, resource augmentation.
- Strong communication and data presentation skills with the ability to build convincing recommendations and sell these to a non-technical audience.
- Self-driven, able to work independently yet acts as a team player
- Able to apply data science principles through a business lens.
- Experience of using R.
- Familiar with, preferably experienced in, Deep Learning (e.g. RNNs, CNNs) or NLP techniques (e.g. TF-IDF, word-embedding).
- Experience developing Generative AI projects.
- Experience of exercising software engineering best practices. E.g. test-driven development, smart data structure and algorithm selection.
- Experience in using cloud environment (e.g. Azure, AWS)..
- Experience using Azure Databricks, Azure MLflow, Azure ML services and/or other ML services.
- Experience using Excel, SQL, PowerBI, Tableau.
- Experience using Docker and Kubernetes.
- Experience working in an Agile development team.
- Experience delivery data science for financial industry or large/complex organisations.