Data Scientist – Analyst

Chubb Ltd.; São Paulo; On-site; BRL 180,000 - 280,000; Full time

Job summary

Chubb External is seeking a Data Scientist in São Paulo, Brazil, to extract actionable insights from complex data and design advanced analytical models and ML solutions that drive business growth.

The role requires building robust data pipelines for model training, scoring, and reporting, plus designing AB tests for incentives and pricing. You will collaborate across channels to translate analytics into business actions and rapidly prototype solutions.

Responsibilities

• Build, validate, and deploy ML propensity and recommendation models using Databricks.
• Develop and maintain robust data pipelines for model training, scoring, and reporting.
• Design and execute AB tests for incentives and pricing; analyze results and provide actionable insights.
• Collaborate with business to gather requirements and request relevant data.
• Work closely with channel teams to translate analytics into business actions.
• Demonstrate a bias to action by rapidly prototyping and iterating on solutions.
• Document methodologies, code, and results for reproducibility and knowledge sharing.

Skills

Machine Learning

Data Analysis

Prototyping

Tools

Databricks

Job description

Role Purpose

The Data Scientist will be responsible for extracting actionable insights from complex data sets to support data-driven decision-making across the organization. This role will design, develop, and implement advanced analytical models and machine learning solutions to solve business challenges, optimize processes, and drive growth.

Key Responsibilities

• Build, validate, and deploy ML propensity and recommendation models using Databricks.
• Develop and maintain robust data pipelines for model training, scoring, and reporting.
• Design and execute AB tests for incentives and pricing; analyze results and provide actionable insights.
• Collaborate with business to gather requirements and request relevant data.
• Work closely with channel teams (OBTM, F2F, Digital) to translate analytics into business actions.
• Demonstrate a bias to action by rapidly prototyping and iterating on solutions.
• Document methodologies, code, and results for reproducibility and knowledge sharing.