Data Science, Manager/ Lead (Insurance, 65k)

Michael Page (Hong Kong); Hong Kong; On-site; HKD 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Insurance exposure

Cloud analytics deployment experience

Enterprise-scale data science projects

Job summary

Michael Page (Hong Kong) is seeking a senior data science leader to design, develop, and deploy machine learning solutions and automated data pipelines. You will drive technical excellence across the DS lifecycle, mentor teams, and ensure scalable production deployments.

You will perform data prep, feature engineering, and customer segmentation to deliver actionable insights and robust predictive models across propensity, churn, and risk scoring.

Qualifications

• Bachelor's or Master's degree in data science, statistics, CS, mathematics, or related field.
• 8+ years in advanced analytics, ML, predictive modeling, and customer analytics.
• Hands-on experience designing and operating ML platforms with MLOps/AIOps best practices.
• Proven track record deploying ML models to production at scale.
• Expertise in feature engineering across large, multi-source datasets.
• Strong Python (Pandas, Scikit-Learn, XGBoost, LightGBM), SQL, and Databricks skills.

Responsibilities

• Lead design, development, and production deployment of ML solutions, automated data pipelines, and customer analytics models.
• Drive technical excellence across the DS lifecycle: architecture, standards, code quality, mentorship.
• Perform data prep, feature engineering, and customer segmentation for actionable insights.
• Develop, validate, and optimize predictive models (propensity, churn, future value, risk scoring).
• Set up model monitoring, track performance, and manage drift.

Skills

Python

SQL

Databricks

MLOps

Model deployment

Feature engineering

Cloud platforms

SHAP explainability

Monitoring & dashboards

Education

Bachelor's or Master's in data science / related

Tools

MLflow

Airflow

CI/CD pipelines

Containerization

Databricks

Job description

• ML in Cloud environment, Churn Prediction Model
• Partner IT, Cloud, Infra - Deployment, Milestone Update

About Our Client

The organisation operates in the insurance industry in Hong Kong.

Job Description

• Lead the design, development, and production deployment of machine learning solutions, automated data pipelines, and advanced customer analytics models.
• Drive technical excellence across the data science lifecycle, including solution architecture, engineering standards, code quality, and team mentorship.
• Perform data preparation, feature engineering, and customer segmentation to generate actionable business insights.
• Develop, validate, and optimize predictive models such as propensity, churn, future value, and risk-scoring models.
• Establish model monitoring frameworks, track performance, and proactively manage model drift and degradation.
• Collaborate with Product, Marketing, and Technology teams to embed analytics into business processes and customer engagement initiatives.
• Translate complex analytical outputs into clear business insights for both technical and non-technical stakeholders.
• Enhance model performance through continuous feature innovation and integration of new data sources.
• Work closely with Cloud, Infrastructure, Cybersecurity, DevOps, and IT teams to ensure scalable, secure, and resilient deployments.
• Manage project deliverables, timelines, milestones, risks, and stakeholder communications.
• Act as a key liaison between business and technical teams to align analytics initiatives with strategic objectives.

The Successful Applicant

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related discipline.
• Min. 8+ years of experience in advanced analytics, machine learning, predictive modelling, and customer analytics.
• Strong hands-on experience designing and operating ML platforms using MLOps/AIOps best practices.
• Proven track record in deploying machine learning models into production environments at scale.
• Expertise in feature engineering and working with large, complex datasets from multiple sources.
• Strong proficiency in Python (Pandas, Scikit-learn, XGBoost, LightGBM), SQL, and Databricks.
• Experience with ML tools and frameworks such as MLflow, Airflow, CI/CD pipelines, containerization, and cloud platforms.
• Knowledge of model explainability techniques (e.g., SHAP) and model governance best practices.
• Experience building monitoring frameworks, model performance dashboards, and drift detection solutions.

What's on Offer

• Insurance (Life & General Insurance) industry exposure.
• Cloud-based analytics and machine learning deployment experience.
• Experience leading enterprise-scale data science transformation initiatives.