Insurance Data Scientist

Placements24; Soweto; Hybrid; ZAR 900,000 - 1,200,000; Full time

Benefits offered by this job

Fully remote work

Professional development budget

Health, dental, and vision insurance

Competitive salary with bonus

Job summary

Placements24 in South Africa seeks an innovative Insurance Data Scientist to join a remote-first team, supporting pricing, risk assessment, fraud detection and customer segmentation within the insurance sector.

You will build predictive models, apply ML algorithms, run experiments, and translate complex findings into actionable visuals for stakeholders, collaborating with actuaries, underwriters and product managers.

Qualifications

• Master's or Ph.D. in Data Science, Statistics, CS or related field.
• Proven experience as a Data Scientist in insurance/financial services.
• Proficiency in Python or R and ML libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with SQL and big data technologies.

Responsibilities

• Develop predictive models for pricing, underwriting, claims analysis, and customer behavior.
• Apply ML algorithms to uncover patterns in large datasets.
• Design and run A/B tests and other experiments to validate hypotheses.
• Clean, transform, and prepare data for analysis with quality controls.
• Collaborate with actuaries, underwriters, and product managers to translate business problems into DS solutions.
• Communicate findings clearly through visualizations and presentations.
• Stay current with advancements in data science, ML and AI.

Skills

Statistical modeling

Machine learning

Python

R

SQL

Big data technologies

Education

Master's degree

PhD

Tools

scikit-learn

TensorFlow

PyTorch

Job description

About the Role

Our client is seeking an innovative and highly skilled Insurance Data Scientist to join their fully remote team, supporting their operations connected to Soweto . In this role, you will leverage advanced statistical analysis, machine learning, and data mining techniques to extract actionable insights from complex datasets. Your work will be instrumental in driving strategic decisions related to pricing, risk assessment, fraud detection, and customer segmentation within the insurance sector. This is a unique opportunity to work on cutting-edge data challenges in a remote-first culture.

Key Responsibilities

• Develop and implement predictive models for pricing, underwriting, claims analysis, and customer behavior.
• Apply machine learning algorithms and statistical techniques to uncover patterns and trends in large datasets.
• Design and execute A/B tests and other experiments to validate hypotheses and measure impact.
• Clean, transform, and prepare data for analysis, ensuring data quality and integrity.
• Collaborate with actuaries, underwriters, and product managers to translate business problems into data science solutions.
• Communicate complex findings and recommendations clearly through visualizations and presentations.
• Stay current with the latest advancements in data science, machine learning, and AI.

Requirements

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Proven experience as a Data Scientist , with a strong focus on statistical modeling and machine learning in the insurance or financial services industry.
• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with SQL and working with large datasets, including data warehousing and big data technologies.
• Strong understanding of insurance principles and common business challenges.
• Excellent analytical, problem-solving, and critical thinking skills.

Benefits

• Highly competitive salary and comprehensive bonus structure.
• Fully remote work arrangement offering maximum flexibility.
• Generous budget for professional development, conferences, and training.
• Comprehensive health, dental, and vision insurance plans.
• A stimulating work environment focused on innovation, collaboration, and data-driven decision-making.