Senior Data Scientist (Azure Data Engineering & MLOps)

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.

Key Responsibilities:

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

Technical Knowledge

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

Behavioural Competencies

• 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

Minimum Experience Required

• 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

Ideal Candidate Profile

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.

Contract Details

• 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: