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Actuarial Business Analyst

Badger Holdings (Pty) Ltd

Wes-Kaap

On-site

ZAR 800 000 - 1 200 000

Full time

Yesterday
Be an early applicant

Job summary

A leading insurance analytics company in South Africa is seeking an Actuarial Business Analyst to lead a team in building impactful machine learning models. You will bridge the gap between technical teams and business strategy, ensuring models deliver measurable value. The ideal candidate has a degree in Actuarial Science, over 7 years of experience in the financial services sector, and strong leadership skills. Join us to influence the future of insurance analytics.

Qualifications

  • Over 7 years of experience in insurance or financial services.
  • Proven track record in developing statistical/machine learning models.
  • Hands-on experience with claims modelling and survival analysis.

Responsibilities

  • Lead and mentor a team of Data Scientists.
  • Oversee the testing and deployment of machine learning models.
  • Communicate results to decision-makers effectively.

Skills

Leadership experience in Data Science
Stakeholder engagement and communication
Building statistical/machine learning models
Knowledge of BI tools (Power BI, Qlik Sense)

Education

Degree or Honours in Actuarial Science

Tools

Python
R
SQL
Snowflake
Job description
Actuarial Business Analyst – Data Science and Machine Learning Lead

George, Western Cape | Badger Holdings SA

At Badger Holdings, we don’t just crunch numbers, we turn them into business game‑changers. We’re on the hunt for an Actuarial Business Analyst who can lead a team of Data Scientists to build machine learning models that move the needle. You’ll be the translator, the strategist, and the impact‑driver, making sure every model delivers measurable value and is understood from boardroom to operations floor. If you can bridge deep technical expertise with commercial impact and lead a high‑performance analytics team, this is your seat at the table.

What You’ll Do
Lead & Inspire
  • Manage, mentor, and grow a team of Data Scientists into a powerhouse of delivery.
  • Build a culture where experimentation, collaboration, and results thrive.
Design & Deliver
  • Oversee the creation, testing, and deployment of machine learning models that solve real business problems.
  • Measure, quantify, and communicate the commercial impact of analytics solutions.
  • Keep models relevant, accurate, and laser‑focused on business goals.
Bridge Tech & Business
  • Be the go‑to liaison between data science and decision‑makers.
  • Translate complex analytics into plain, actionable English.
  • Ensure operational teams adopt and use model outputs effectively.
Drive Excellence
  • Gather, refine, and document requirements for analytics projects.
  • Push for best practices, smarter modelling approaches, and continuous process improvement.
Essential
What You’ll Need to Bring
  • Degree or Honours in Actuarial Science.
  • 7+ years’ experience in insurance or financial services (short‑term insurance? Even better).
  • Proven track record in building statistical/machine learning models.
  • Skill in turning model outputs into business gold.
  • Leadership experience in Data Science, Analytics, or Actuarial teams.
  • Experience with claims modelling & survival analysis in insurance.
  • BI tool mastery (Power BI, Qlik Sense, Tableau, etc.).
  • Exceptional stakeholder engagement and communication skills.
Preferred
  • Hands‑on skills in Python, R, SQL, and cloud platforms like Snowflake.
  • Experience shaping both technical and business requirements.
  • Strong grasp of data‑driven decision frameworks.
Why Badger?

We’re bold. We’re fast. We’re building the future of insurance analytics and you’ll be at the centre of it. Here, your models won’t gather dust in a slide deck. They’ll shape strategy, impact the bottom line, and change how we work.

Ready to lead the charge? Apply now and turn data into decisive action.

The position will be filled in line with Badger Holdings’ culture, values, and Employment Equity policy. Preference will be given to candidates from under‑represented designated groups.

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