Manager, ML Engineer (Data Science), AI Scaling and Transformation, Engineering, AI & Data, Technology & Transformation

Overview

As an ML Engineer in Deloitte’s AI Scaling and Transformation team, you design, build and scale AI/ML solutions for public safety and security clients in regulated environments. You own end-to-end ML workstreams and translate business needs into robust ML capabilities. You will deploy production-ready models, implement MLOps, and ensure responsible AI governance, working with cross-functional teams to deliver impactful outcomes.

Pay / Benefits

• hybrid working policy
• flexible working arrangements
• career progression and world-class development
• wellbeing support and inclusive culture
• supportive and collaborative environment

Responsibilities

• Own ML and data science workstreams aligned to client priorities and quality standards
• Translate operational challenges from stakeholders into practical ML solution designs
• Design, build, test and deploy scalable ML models and data pipelines
• Operationalise ML solutions with MLOps: monitoring, versioning, CI/CD, testing, retraining
• Apply responsible AI, explainability, security, privacy, and governance in development lifecycle
• Collaborate with architects, data engineers, data scientists and delivery leads to integrate ML into broader tech landscape
• Manage stakeholders and communicate progress, risks, and recommendations clearly

Key requirements

• Degree or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, AI, ML or related discipline
• Hands-on ML/data science solution delivery in consulting, public sector or tech delivery
• Experience owning technical deliverables and coordinating multidisciplinary teams
• Ability to translate requirements into practical recommendations for technical and business stakeholders
• Experience in secure, regulated environments; eligible for security clearance if needed
• Strong Python or other data science language proficiency
• Practical experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow
• End-to-end ML lifecycle experience: data prep, feature engineering, model development, deployment, monitoring, optimization
• MLOps experience: CI/CD, model versioning, automated testing, retraining, monitoring
• Experience with cloud platforms and data science environments (Azure, AWS, GCP, Databricks)
• Strong communication and stakeholder management skills

• Strong communication and stakeholder management
• Problem-solving能力
• Ability to explain complex modelling to technical and non-technical audiences
• Python or equivalent
• scikit-learn
• XGBoost

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