Senior Data Scientist

ROLE PURPOSE

As a Senior Data Scientist at Lula you will turn data into decisions across the business. You will work with teams in product operations finance marketing and credit to frame problems build analyses and models and get the results into the hands of the people who act on them.

Your focus is practical data science with a strong machine learning edge: finding the signal proving it with sound statistics and making sure the models we rely on run reliably in production. You will work alongside our data and analytics engineers set the standard for how data science is done at Lula and help grow small and medium enterprises in South Africa through better use of data.

KEY RESPONSIBILITIES

• Partner with stakeholders to translate business questions into analytical problems and communicate findings and recommendations clearly to technical and non-technical audiences.
• Own the productionisation of machine learning models including reproducible pipelines versioned code automated testing and deployment working with data engineers and analytics engineers.
• Monitor models in production for performance drift and data quality and drive retraining remediation and improvement when they degrade.
• Build validate and maintain predictive models (e.g. classification regression forecasting segmentation).
• Explore clean and analyse large datasets to uncover trends drivers and opportunities.
• Design and analyse experiments (A/B tests pilots) and measure the impact of business initiatives.
• Build dashboards reports and reusable analysis assets that make insights self-serve.
• Help raise the standard for how data science is done at Lula
• Contribute to data quality documentation and good practice across the data team.

THE SKILLS AND EXPERIENCE WERE LOOKING FOR

• A numerical or quantitative degree (e.g. mathematics statistics data science computer science engineering economics finance)
• 3-5 years experience in a data science or advanced analytics role
• Proven experience productionising machine learning models and monitoring them once live (performance drift data quality)
• Experience building and evaluating machine learning models on real-world data
• At least 3 years experience actively using Python in data analysis or modelling (pandas scikit-learn or similar)
• At least 3 years experience actively using SQL for data analysis
• Experience applying software engineering practices to data science work (Git code review automated testing)
• Experience in FinTech lending or small business environments or a strong interest in the space

Advantageous

• Familiarity with MLOps tooling such as MLflow DVC or similar (experiment tracking model registry monitoring)
• Experience with dbt and CI/CD (e.g. GitHub Actions)
• Exposure to cloud data platforms (Azure AWS or GCP) and cloud data warehouses (e.g. Snowflake)
• Experience with notebooks and collaborative analytics tools (e.g. Hex)
• Exposure to Docker workflow orchestration or infrastructure as code
• Experience mentoring or technically guiding other data professionals

OUR TECH STACK

• Azure (Functions databases blob storage)
• DBT
• Snowflake
• Airflow
• Airbyte
• Event Grid