(Sr.) Data Scientist - Top notch financial institution

Talent Pro Recruitment Company Limited; Hong Kong Island; On-site; HKD 600,000 - 900,000; Full time

Job summary

Talent Pro Recruitment Company Limited is seeking a Senior Data Scientist for a leading financial institution in Hong Kong. You will design segmentation and scoring frameworks, perform end-to-end data wrangling, feature engineering, and develop interpretable models for business decisions.

You will own model monitoring, productionise pipelines in the cloud, and collaborate with product and marketing teams to translate analytic outputs into actionable customer insights.

Qualifications

• Experience building segmentation and scoring models in financial services.
• Strong data wrangling and feature engineering skills.
• Ability to translate complex analytics to non-technical stakeholders.

Responsibilities

• Design customer segmentation and scoring frameworks across insurance product lines.
• Build, validate, and refine machine learning models with interpretable outputs.
• Own model monitoring and performance tracking to address data drift.
• Productionise models in a cloud environment with automated pipelines.
• Collaborate with product, marketing, and technology teams to integrate outputs into workflows.

Skills

Data Science

Machine Learning

Data Wrangling

Feature Engineering

Model Deployment

Model Monitoring

Tools

Python

SQL

Cloud

Job description

(Sr.) Data Scientist - Top notch financial institution

Design and develop customer segmentation and scoring frameworks using structured data across multiple insurance product lines

Perform end-to-end data wrangling, feature engineering, and the development of tagging and classification logic to identify customer cohorts with business significance

Build, validate, and continuously refine machine learning models, ensuring outputs are interpretable and actionable for non-technical stakeholders

Develop propensity and future value models to support forward-looking customer strategies, including cross-sell and retention programmes

Own model monitoring, validation, and performance tracking over time, proactively addressing data drift and model decay

Automate data pipelines to operationalize model outputs and enable scalable, repeatable processes

Partner with product, marketing, and technology teams to integrate model outputs into operational workflows and campaign tooling

Translate complex analytical results into clear, explainable customer narratives and profile artefacts for business use

Enhance and expand predictive features by integrating new data sources and refining existing signals to improve model quality and coverage

Refresh and rebuild churn prediction models, ensuring they remain accurate, robust, and aligned with evolving business dynamics

Productionise models in a cloud environment with automated pipelines, enabling scalable and reliable delivery of scoring outputs

Design and maintain monitoring dashboards to track model performance in production, identify degradation, and feed insights back into the modelling cycle

Develop interpretable, customer-level risk indicators across multiple behavioural and financial dimensions to support campaign targeting

Partner with retention and campaign management teams to integrate model outputs into operational systems and workflows

Maintain and update models on a regular cadence to combat data drift and reflect changes in underlying customer behavior or data infrastructure

Collaborate with cross-functional stakeholders to align analytical outputs with business priorities

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