Jobs at Badger Holdings (Pty) Ltd

Your day-to-day will include:

• Preparing and transforming data for machine learning (cleaning, feature engineering, optimisation)
• Conducting exploratory analysis to uncover trends and guide model design
• Building, testing, and deploying machine learning models aligned to business needs
• Collaborating with actuarial teams to align assumptions and modelling approaches
• Validating models and ensuring performance, accuracy, and reliability
• Monitoring live models for drift and performance degradation
• Maintaining, retraining, and improving models over time
• Creating clear documentation for models, pipelines, and processes
• Developing dashboards and reporting tools to track performance and KPIs
• Communicating insights and recommendations to both technical and non-technical stakeholders

Requirements:

• A degree (Honours preferred) in Data Science, Actuarial Science, Statistics, Mathematics, or a related field
• 3+ years’ experience in data science or machine learning roles
• Strong Python and R skills, with proven experience building and deploying models
• Solid experience across the full ML lifecycle (data → model → deployment)
• Hands-on experience with statistical modelling, machine learning, and time series forecasting
• The ability to translate technical outputs into clear business value
• Strong communication and stakeholder engagement skills, particularly with actuarial teams
• A proactive, energetic mindset and a genuine passion for data and innovation

Nice to have:

• Experience in insurance (especially short-term insurance)
• Exposure to CLTV modelling, claims modelling, survival analysis, or actuarial-related work
• Experience with dbt, Snowflake, GCP, or similar cloud platforms
• Familiarity with MLOps practices and machine learning platforms
• Experience with BI tools (Power BI, Tableau, Looker, Qlik, etc.)

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Your day-to-day will include:

• Designing and building scalable ELT pipelines from multiple data sources
• Developing clean, well-structured data models using dbt
• Optimising data models and workloads within Snowflake
• Transforming raw data into high-quality, analytics-ready datasets
• Implementing data quality checks, monitoring, and observability
• Improving performance and managing cloud cost efficiency
• Applying engineering best practices (CI/CD, testing, code reviews)
• Collaborating with cross-functional teams to deliver practical, scalable solutions
• Contributing to standards, documentation, and mentoring junior engineers

Requirements:

• 4–6+ years’ experience in Data Engineering or a similar role
• Strong SQL skills and experience working with complex transformations
• Proficiency in Python (or similar) for data processing and automation
• Experience building ELT pipelines in cloud environments (AWS, Azure, or GCP)
• Hands-on experience with modern data warehouses (e.g. Snowflake, Synapse)
• Familiarity with tools like dbt, Airflow, or Dagster
• Experience working with structured and semi-structured data (JSON, Parquet)
• A solid understanding of data governance, security, and best practices

Bonus if you’ve worked with:

• Real-time/streaming technologies (Kafka, Spark Streaming)
• BI tools like Qlik, Power BI, Looker, or Tableau
• Qlik Replicate / Compose or similar tools
• Agile delivery environments (Jira, Azure DevOps)
• Cloud certifications or exposure to DevOps practices