Senior Data Scientist (Azure Data Engineering & MLOps)
dvtcareers
Cape Town, Western Cape, South Africa · Cape Town · South Africa
Posted Jul 22, 2026
- Contract
- Hybrid
- Data and Analytics & RPA (DAT)
- experienced
dvtcareers
Cape Town, Western Cape, South Africa · Cape Town · South Africa
Posted Jul 22, 2026
DVT is a leading technology consulting and software engineering company delivering innovative solutions across Africa and internationally. We partner with clients to solve complex business challenges through software engineering, cloud platforms, data, artificial intelligence, and digital transformation. Our teams combine deep technical expertise with a strong consulting mindset to create measurable business value.
We are seeking a Senior Data Scientist with strong Azure Data Engineering and MLOps expertise to join a client engagement on an initial 12 month contract. This is not a traditional data science role focused solely on model development. The ideal candidate will be a well-rounded practitioner capable of operating across the full data and machine learning lifecycle, from data ingestion and engineering through to model deployment, monitoring, and optimisation.
This role is a critical addition to the client's team. While the existing team possesses strong data science capabilities, there is a significant need for someone who can strengthen the data engineering foundations while still contributing to advanced analytics and machine learning initiatives.
The successful candidate will be able to design and build scalable data platforms, develop machine learning solutions, implement MLOps practices, and work closely with stakeholders to translate business challenges into production-ready data solutions.
Data Engineering (Primary Focus)
Design, develop and maintain scalable Azure-based data platforms and pipelines
Build and optimise batch and real-time data ingestion processes
Develop robust ETL and ELT solutions using Azure services
Create and manage data models that support analytics, machine learning and reporting requirements
Ensure data quality, reliability, governance and performance across the data ecosystem
Collaborate with data consumers and business stakeholders to understand and meet data requirements
Data Science & Machine Learning
Develop predictive and prescriptive models to address complex business challenges
Perform exploratory data analysis and feature engineering on large and complex datasets
Design, evaluate and improve machine learning models and algorithms
Conduct experiments and validation exercises to measure solution effectiveness
Translate analytical findings into actionable business recommendations
Communicate technical results and insights to both technical and non-technical stakeholders
MLOps & Productionisation
Design and implement machine learning deployment frameworks and CI/CD processes
Automate model training, testing, deployment and monitoring workflows
Establish model governance, versioning, observability and performance monitoring practices
Implement reproducible machine learning environments and experimentation frameworks
Support the transition of machine learning models from proof of concept to production
Drive best practices for scalable and maintainable AI solutions
Strong experience with:
Python and SQL
Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage, Azure Machine Learning, Azure Functions, Event Hub and related Azure data integration services
Spark and distributed data processing frameworks
Machine learning frameworks such as Scikit-learn, TensorFlow or PyTorch
Data modelling, warehousing and large-scale data architecture
DevOps and CI/CD tools supporting machine learning and data engineering workloads
Git and collaborative development practices
Good understanding of:
Supervised and unsupervised machine learning techniques
Feature engineering and model optimisation
Statistical analysis and experimentation
Data governance, security and compliance principles
Cloud-native architecture patterns
Modern software engineering practices
Strong problem-solving and analytical thinking skills
Ability to work independently and take ownership of complex technical solutions
Consulting mindset with strong stakeholder engagement skills
Excellent communication and presentation abilities. Comfortable operating across multiple disciplines including data engineering, data science and MLOps
Ability to translate business requirements into scalable technical solutions
Passion for continuous learning and emerging technologies
Strong collaboration and mentoring capabilities
Results-oriented with a focus on delivering business value
5+ years of experience in Data Science, Machine Learning, Data Engineering or related fields
Demonstrable hands-on experience building Azure-based data platforms and data pipelines
Proven experience deploying and operationalising machine learning solutions
Strong understanding of MLOps principles and practices
Experience working across the end-to-end machine learning lifecycle
Experience with large-scale data processing and cloud-native architectures
Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics or a related discipline
Relevant Azure certifications would be advantageous
The ideal candidate is a rare combination of Data Scientist, Azure Data Engineer and MLOps practitioner. They are equally comfortable building enterprise-grade data pipelines, developing machine learning models, and deploying those models into production environments.
Most importantly, they will strengthen the client's data engineering capability while continuing to contribute meaningfully to advanced analytics and machine learning initiatives. Their ability to bridge the gap between data engineering, data science and operationalisation will be critical to the success of the engagement.
Initial contract duration: 12 months
Engagement focused on Azure-based data and AI solutions
Opportunity to work on complex enterprise-scale data and machine learning initiatives
Collaborative environment alongside experienced data science professionals
Who we are: