Data Science and Engineering Lead

Prudential's purpose is to help people get the most out of life. We will deliver our purpose by creating a culture in which diversity is celebrated and inclusion assured, for our colleagues, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and in exchange, we support our people's career ambitions. We pledge to make Prudential a place where you can Connect, Grow and Succeed.

Job Profile Summary:
This role will play a critical part in the Data Science workstream within the Analytics Centre of Excellence (CoE). The role is responsible for progressing Prudential Singapore’s business growth with data engineering, statistical analysis and machine learning methods. The role is required to work with team members to contribute in all areas of the machine learning process including data engineering, model development, deployment and monitoring.

**Job Description**:

- Lead the development, validation, deployment and management of machine learning, statistical models, and business analysis, as well as management of analytics jobs handled by the Analytics CoE.
- Lead development of deep insights about channel performance by PACS customer profile, case size distribution of products sold in different channels, trend and seasonality, geospatial analysis.
- Lead development of insights into the acquisition, conversion or retention of a customer across marketing and operations by continuously analyzing the end-to-end customer journeys.
- Build, optimize and manage ETL workflows that streamline business processes and create new value for the business.
- Develop predictive modelling, statistical analysis, data reports, and performance metrics.
- Ensure customers are not adversely affected by machine learning algorithms by following FEAT principles with a focus on Singapore’s Veritas Guidelines.
- Create models and define metrics to segment and distributors and customers to increase conversion, cross-sell and generally drive sustainable growth.
- Representing team and close collaboration with business partners and recommending AIDA solution to solve business problems and achieve business goals.
- Growing team skills through examining code and enforcing development standards and patterns

Who we are looking for:

- Subject matter expertise in end-to-end machine learning processes and technologies such as DataBricks, MLFlow, Azure ML, SageMaker, Vertex AI, TFX etc.
- Subject matter expertise in common machine learning methods (linear/logistic regression, clustering, classification, principal component analysis, association rules, recommendation techniques).
- Deep proficiency in at least one common machine learning language, including Python, Scala, or PySpark (and SQL)
- In-depth knowledge of advanced statistical theories, methodologies, and inference tools (e.g. familiar with hypothesis testing, (generalized) linear models, additive models, mixture models, non-parametric models, etc.)
- Strong in SQL.
- Familiarity in data visualization libraries or tools such as Matplotlib and plot.ly
- Azure experience
- Creating documentation, attribute mapping documents, functional specifications.
- Version control tools (Git)
- DevOps mindset
- Translating business problems and use cases into appropriate models
- Presenting results and recommendations to non-technical business partners and stakeholders to drive decision-making and actions

**Qualifications**:

- Proven track record of at least 3-5 years in in a similar role
- Master’s degree or PhD or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline.
- Knowledge of life insurance practices preferred