Manager, ML Engineer (Data Science), AI Scaling and Transformation, Engineering, AI Data, Technology Transformation
Actively Hiring
Full-time $24k Posted about 2 months ago
Responsibilities
- check_circle Own the delivery of machine learning and data science workstreams, ensuring outputs are aligned to client priorities, delivery plans and quality standards.
- check_circle Work with client stakeholders, product owners and technical teams to understand operational challenges and translate them into practical ML solution designs.
- check_circle Design, build, test and deploy machine learning models, analytical pipelines and data science components that are robust, scalable and maintainable.
- check_circle Support the operationalisation of ML solutions through MLOps practices, including model monitoring, versioning, automated testing, CI/CD and performance optimisation.
- check_circle Apply responsible AI, model explainability, security, privacy and governance considerations throughout the development lifecycle, particularly within secure and regulated environments.
- check_circle Collaborate with solution architects, data engineers, data scientists, business analysts and delivery leads to integrate ML solutions into wider technology and business landscapes.
- check_circle Manage stakeholders and contribute to delivery leadership by communicating progress, risks, dependencies and technical recommendations clearly and confidently.
Basic qualifications
- Degree or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence, Machine Learning or a related discipline.
- Hands-on experience designing, developing and deploying machine learning or data science solutions in a consulting, commercial, public sector or technology delivery environment.
- Experience owning technical deliverables or workstreams, managing priorities and coordinating across multidisciplinary teams.
- Demonstrated ability to work with business and technical stakeholders to understand requirements, shape solution options and translate analytical outputs into practical recommendations.
- Experience delivering within secure, regulated or complex public sector environments is advantageous; candidates must be eligible and willing to obtain or hold the required security clearance for relevant client engagements.
Preferred qualifications
- Strong proficiency in Python or another modern programming language used for data science and machine learning development.
- Practical experience with machine learning frameworks and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow or equivalent.
- Experience across the end-to-end ML lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring and optimisation.
- Understanding of MLOps and production ML practices, including CI/CD, model versioning, automated testing, retraining, monitoring and reproducible delivery.
- Experience using cloud platforms and data science environments such as Azure, AWS, GCP, Databricks or equivalent to build and deploy scalable ML solutions.
- Strong communication, stakeholder management and problem-solving skills, with the ability to explain complex modelling approaches, trade-offs and outcomes to technical and non-technical audiences.
Tags & Focus Areas
Remote Ai Machine Learning Data Science
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