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Senior Data Scientist

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Onsite

Senior Data Scientist

Lula

Senior Data Scientist

Skills

Google Cloud PlatformSQL QueriesDockerPythonData SciencepandasSnowflakeData AnalysisMachine LearningAWSGitHubApache AirflowAzure

18 days ago

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Important information


Contract type:

Permanent

Location:

City of Cape Town, Cape Town, South Africa

Work mode:

Onsite

Published on:

8 October 2026

What they need


About Lula

Lula is on a mission to simplify business funding for South African SMEs. We build fast, digital-first fintech tools that make cash flow management seamless so business owners can focus on doing what they love. We’re a fast-growing, tech-driven team that values curiosity, collaboration, and high impact over red tape. Speaking of love, we’re looking for Lulas who love to make a difference to join our team and change the game.

Culture Code

  • We Embrace Curiosity

    We continuously seek better ways to deliver value with a solutions-over-problems mindset.
  • We win as One
    • We collaborate, build strong relationships and value diverse perspectives
  • We’re Driven by Purpose

    We are passionate and committed to delivering the best products and services for SMEs
  • We Execute with Ambition
    • We set ambitious goals, embrace challenges, and deliver with focus and determination.

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 WE’RE 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

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