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About the Role

We’re looking for a DP-100–certified Azure Data Scientist who is passionate about applied Machine Learning and delivering measurable improvements for clients through smart, efficient, and scalable solutions. You’ll be part of a growing innovation and technology team, contributing to the design, training, and deployment of AI and ML models within the Azure ecosystem.

Using your expertise in Azure Machine Learning, Python (pandas, scikit-learn), and Azure SDKs, you’ll help shape our data and productivity capabilities — integrating ML models into real business processes that drive meaningful outcomes.

This role is ideal for someone who combines technical depth, curiosity, and a commitment to continuous learning, eager to build a long-term career in a forward-thinking, collaborative environment.

Responsibilities

Work directly with business stakeholders to design and deliver end-to-end machine learning solutions aligned with operational and governance standards.
Prepare and transform datasets for modelling using Python and Azure-native tools, ensuring data quality and consistency across the ML lifecycle.
Build, evaluate, and deploy models using AutoML, MLflow, and custom pipelines, focusing on performance, scalability, and maintainability.
Implement monitoring and retraining strategies to detect drift and maintain model accuracy over time.
Apply responsible AI principles — fairness, explainability, and privacy — throughout model development.
Collaborate with cross-functional teams to integrate ML and AI solutions into business workflows, enhancing decision-making, engagement, and productivity.
Support clients in maintaining and optimising Azure ML environments for reproducible experimentation and efficient deployment.
Contribute to MLOps implementation, including CI/CD pipelines, model registry, and environment management across dev/test/prod environments.
Work with tools such as Azure DevOps, GitHub Actions, Azure Storage, and Power BI/Fabric to ensure solutions are production-ready and observable.
Stay current with emerging Azure AI technologies, contributing to the continuous improvement of our machine learning practices.

Requirements

Microsoft Certified: Azure Data Scientist Associate (DP-100) — mandatory.
2+ years of experience in applied machine learning or data science, delivering production-grade ML solutions.
Hands-on experience designing, training, and deploying models using Azure Machine Learning (AML), AutoML, and MLflow.
Proficiency in Python, pandas, and scikit-learn for feature engineering, model training, and rigorous validation.
Proven ability to deploy models to real-time REST endpoints and orchestrate batch inference pipelines.
Experience implementing MLOps practices, including CI/CD pipelines, model registries, and automated retraining.
Understanding of Azure cloud and data engineering concepts, including compute, storage, and orchestration services.
Strong collaboration skills — able to work with subject matter experts to translate business processes into ML opportunities with measurable impact.
Excellent communication and problem-solving abilities, with confidence engaging technical and non-technical audiences.
Knowledge of responsible and ethical AI frameworks, ensuring transparency and compliance in model development.

Nice to Have

Exposure to Kubernetes, Azure AI Search, or Azure AI Foundry for scalable ML deployments.
Familiarity with generative AI techniques and language model optimisation.
Experience integrating analytics or model outputs into Power BI or Azure Fabric dashboards.
Understanding of Azure cost governance and performance optimisation for ML workloads.
Broader knowledge of data governance and security principles in AI-driven environments.

Benefits