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As a Senior Data Scientist at Lulalend, you will transform data into actionable business decisions across the organization. You will partner with product, operations, finance, marketing, and credit teams to frame analytical problems, build robust models, and deliver insights to stakeholders who act on them. Your work combines practical data science with a strong machine learning focus: identifying signal in data, validating findings with sound statistics, and ensuring models perform reliably in production.
Key responsibilities include:
- Translating business questions into analytical problems and communicating findings clearly to both technical and non-technical audiences
- Owning the end-to-end productionization of machine learning models, including reproducible pipelines, version control, automated testing, and deployment in collaboration with data and analytics engineers
- Monitoring production models for performance degradation, data drift, and quality issues; driving retraining and remediation as needed
- Building, validating, and maintaining predictive models (classification, regression, forecasting, segmentation)
- Exploring, cleaning, and analyzing large datasets to uncover trends, drivers, and opportunities
- Designing and analyzing experiments (A/B tests, pilots) to measure business impact
- Creating dashboards, reports, and reusable analysis assets that enable self-serve insights
- Raising standards for data science practices across the team
- Contributing to data quality, documentation, and best practices across the data organization
You will work alongside data and analytics engineers, help set the standard for data science at Lulalend, and contribute to growing small and medium enterprises in South Africa through better data utilization.
Tech stack: Azure (Functions, databases, blob storage), DBT, Snowflake, Airflow, Airbyte, Event Grid.
REQUIREMENTS:
- Numerical or quantitative degree (mathematics, statistics, data science, computer science, engineering, economics, finance)
- 3–5 years' experience in a data science or advanced analytics role
- Proven experience productionizing machine learning models and monitoring them in production (performance, drift, data quality)
- Experience building and evaluating machine learning models on real-world data
- At least 3 years actively using Python for data analysis or modeling (pandas, scikit-learn or similar)
- At least 3 years actively using SQL for data analysis
- Experience applying software engineering practices to data science (Git, code review, automated testing)
- Experience in FinTech, lending, or small business environments, or strong interest in the space
ADVANTAGEOUS:
- Familiarity with MLOps tooling (MLflow, DVC, or similar)
- Experience with dbt and CI/CD (e.g., GitHub Actions)
- Exposure to cloud data platforms (Azure, AWS, GCP) and cloud data warehouses (Snowflake)
- Experience with notebooks and collaborative analytics tools (Hex)
- Exposure to Docker, workflow orchestration, or infrastructure as code
- Experience mentoring or technically guiding other data professionals
