A world's leading Insurance firm is looking for an experienced Senior Expert Data Scientist (MLOps) in the Data Science

Team.

Responsibilities

- Implement and develop AI industrialization, ensuring that all our Data Models are properly deployed on our MLOps platform (Databricks) and in line with our Target Data architecture, ensuring maximum efficiency of models.

- Implement and manage sophisticated data science tools to ensure AI industrialization (models easily deployable, scalable, monitored, retrained). Responsible for the implementation of our MLOps and LLMOps architecture.

- Understand and peer-review data models developed to solve business challenges (incl. machine learning, LLM, GenAI). Finalize AI models prototypes developed by other Data Scientists.

- Work with other departments (IT and business) to ensure operational use of results and measure impacts on key business indicators.

- Work closely with business experts to understand the operational needs (actuarial, marketing, usage…) and translate business needs into mathematical models and problems.

【Company Details】

Our client is one of the world's largest insurance - financial groups, trusted by over 50 million customers. The company provides various financial protections including general insurance, life insurance, retirement funds, and inheritance throughout the lifetime for individual customers, small businesses, and large companies.

【Working Hours】
9:00am - 5:00pm Mon-Fri (Hybrid: 2 days in office and 3 days WFH every week)

【Holidays】
Saturday, Sunday, and National Holidays, Year-end and New Year Holidays, Paid Holidays, Other Special Holidays

【Services / Benefits】

Social insurance, Transportation Fee, No smoking indoors allowed (Designated smoking area), etc.

- Ability to model the reality of business into equations, and to understand the implications and limitations of a model

- Programming skills (ideally Python or similar; appreciated: R, Java/ Scala)

- Strong exp. in data processing (e.g., data mining, social network analysis, text mining…)

- Practice with database access (SQL; optionally Pig and Hive)

- Knowledge and practice of machine learning, including libraries (e.g., scikit-learn, Spark MLlib, R packages)