Senior Data Scientist: GenAI-Driven ML on Databricks

Sabenza IT & Recruitment

Johannesburg

On-site

ZAR 800,000 - 1,200,000

Full time

14 days+
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Job summary

Sabenza IT & Recruitment in Johannesburg, South Africa is seeking a Data Scientist to design, develop and optimise ML and AI solutions that improve customer outcomes and operational efficiency across Personal and Private Banking channels.

You will focus on feature engineering, model development, experimentation and decision science using the Databricks platform to create scalable, reusable analytics assets.

Qualifications

  • Degree in data science, CS, math, stats, engineering, actuarial science, econometrics or related field.
  • Strong hands-on Databricks + Python + SQL + ML experience.
  • Experience taking data science solutions from concept to production.
  • Strong statistical modelling, feature engineering and model validation skills.

Responsibilities

  • Build and optimise ML models using Databricks, MLflow, Python & SQL.
  • Develop customer, propensity, retention, risk, fraud and decisioning models.
  • Build reusable features using Databricks Feature Store.
  • Apply Generative AI & LLMs to innovative business use cases.
  • Turn complex data into actionable insights and measurable business value.
  • Partner with business, product, risk and technology teams.
  • Mentor and inspire junior Data Scientists.

Skills

Databricks
Python
SQL
Machine Learning
Feature engineering
Communication

Education

Degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, Actuarial Science, Econometrics or a related field

Tools

Databricks
MLflow
Python
SQL

Job description

Sabenza IT & Recruitment in Johannesburg, South Africa is seeking a Data Scientist to design, develop and optimise ML and AI solutions that improve customer outcomes and operational efficiency across Personal and Private Banking channels.

You will focus on feature engineering, model development, experimentation and decision science using the Databricks platform to create scalable, reusable analytics assets.

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