Senior Data Scientist

About the Role

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.

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