AI Data Scientist — Insurance Analytics & ML Projects

Ambition; Hong Kong; On-site; HKD 900,000 - 1,300,000; Full time

Job summary

Ambition is seeking a Data Scientist focused on AI projects within the Insurance space. You will develop, implement, and maintain AI/ML solutions to enhance business performance and strategic initiatives, and collaborate with business units to translate requirements into scalable outcomes.

You will analyze large datasets, design dashboards and data models, and implement DP practices to ensure data quality across sources.

Qualifications

• Bachelor's or Master's degree in Data Science, CS, Statistics, Mathematics, Engineering, Business Analytics, or related quantitative discipline.

Responsibilities

• Develop, implement, and maintain AI/ML solutions for the Insurance space.
• Analyze large datasets to generate actionable insights and improve decision-making.
• Design, build, and maintain Power BI dashboards, reports, and data models including DAX development.
• Collaborate with cross-functional stakeholders to understand business requirements and deliver data-driven solutions.
• Ensure data quality, accuracy, and reliability across multiple data sources and reporting environments.
• Present analytical findings, recommendations, and project updates clearly to management and stakeholders.

Skills

Python

SQL

Machine learning

Data analysis

Generative AI

LLMs

Agentic AI

Intelligent automation

Forecasting

Classification

Regression

Clustering

Predictive modelling

Data visualization

Education

Bachelor's or Master's degree in Data Science / CS / Stats / Math / Engineering / Business Analytics or related quantitative discipline

Job description

Ambition is seeking a Data Scientist focused on AI projects within the Insurance space. You will develop, implement, and maintain AI/ML solutions to enhance business performance and strategic initiatives, and collaborate with business units to translate requirements into scalable outcomes.

You will analyze large datasets, design dashboards and data models, and implement DP practices to ensure data quality across sources.