EIRE Systems (Hong Kong) Limited Hiring! Full Time (Senior) Data Scientist - FS-Insurance in Hong Kong - Ricebowl

About the role

In this role, you will act as the predictive engine for customer lifecycle management across multiple product lines. You will bridge advanced machine learning with tangible business outcomes—building, productionising, and monitoring predictive models that drive cross-sell conversion, retention strategies, and churn mitigation.

This is a production-focused data science role where you will design scalable data pipelines in Databricks, deploy robust models, and collaborate directly with product, marketing, and retention teams to turn analytical insights into high-impact operational workflows.

Key responsibilities

• Customer Analytics & Scoring Frameworks: Design, develop, and refine customer segmentation, propensity, future value, and risk-scoring frameworks across multi-product portfolios using structured transactional, behavioral, and intermediary data.

• Predictive & Churn Modeling: Build, evaluate, and refresh machine learning models (including classification, gradient boosting, and survival models) to proactively predict customer churn, lifetime value, and cross-sell opportunities.

• Feature Engineering & Pipeline Automation: Perform end-to-end feature engineering and construct automated ETL/ML data pipelines in cloud environments (Databricks) to operationalise model outputs at scale.

• MLOps & Model Governance: Own model deployment, scheduling, orchestration, and continuous performance monitoring (handling data drift, feature decay, and model degradation in production).

• Explainability & Business Integration: Utilize model explainability techniques (e.g., SHAP) to translate complex model drivers into clear, interpretable business narratives and campaign targeting logic for marketing, retention, and executive stakeholders.

• Cross-Functional Collaboration: Partner closely with product management, marketing, CRM, and technology teams to integrate scoring outputs directly into operational systems and campaign tooling.

About you

• At least 4 years of experience in the data science space

• Proven track record developing and productionising predictive ML models (Classification, Regression, Gradient Boosting e.g., XGBoost/LightGBM, Survival Analysis).

• Expertise in Python (pandas, scikit-learn), SQL, and Databricks / PySpark handling large multi-source datasets.

• Hands-on experience with MLOps practices, orchestration, and model tracking tools (e.g., MLflow, Airflow, CI/CD).

• Solid understanding of model drift detection frameworks and explainability methods (SHAP / LIME).

• Demonstrated experience in customer analytics, CRM, or customer lifecycle/churn modeling.

• Prior exposure to Insurance (Life & Health, General Insurance) or Financial Services is highly advantageous.

• Proven ability to map machine learning outputs directly to measurable business KPIs (e.g., campaign conversion, retention rates).

• Strong stakeholder management and ability to translate complex technical findings into non-technical business narratives.