Job Opportunity
We're seeking a skilled data scientist to design, train, and deploy scalable machine learning solutions using Azure.
This role involves working directly with business stakeholders to understand operational and governance standards, preparing and transforming datasets for modeling, building and deploying models using AutoML, MLflow, and custom pipelines, and implementing monitoring and retraining strategies to maintain model accuracy over time.
As a remote data scientist, you'll collaborate with cross-functional teams to integrate machine learning and AI solutions into business workflows, enhancing decision-making, engagement, and productivity.
Key responsibilities include:
* Designing and delivering end-to-end machine learning solutions aligned with operational and governance standards
* Preparing and transforming datasets for modeling using Python and Azure-native tools
* Building, evaluating, and deploying models using AutoML, MLflow, and custom pipelines
* Implementing monitoring and retraining strategies to detect drift and maintain model accuracy over time
* Collaborating with cross-functional teams to integrate ML and AI solutions into business workflows
* Supporting clients in maintaining and optimizing Azure ML environments for reproducible experimentation and efficient deployment
* Contribution to MLOps implementation, including CI/CD pipelines, model registry, and environment management across dev/test/prod environments
* Staying current with emerging Azure AI technologies and contributing to the continuous improvement of machine learning practices
Requirements include:
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.
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.
Benefits include flexible shifts, no weekend work, day one benefits, mentorship and community support, true belonging, and more.