Role Overview

We are seeking a highly technical Data Scientist with 5+ years of experience who specializes in data engineering, robust pipeline implementation, and data lifecycle management. In this role, you will act as a high-level individual contributor responsible for designing scalable data architectures, optimizing complex ETL/ELT pipelines, and resolving deep-seated data issues within a highly regulated environment.

While you possess a foundational understanding of data science and analytics, your primary focus will be building the stable, high-performance data infrastructure and automated management systems required to support downstream modeling, analytics, and business-critical operations. The ideal candidate is a master of Python, PySpark, SQL, and data orchestration tools .

Key Responsibilities

Pipeline Implementation: Design, build, scale, and maintain automated ETL/ELT data pipelines to ingest and transform large, complex datasets .
Data Infrastructure Management: Manage and optimize core applications and data services supporting IBG, CBG, and enterprise data systems .
Data Issue Management: Diagnose, troubleshoot, and resolve deep-seated data issues, pipeline bottlenecks, and complex reconciliation gaps .
System Optimization: Continuously monitor, benchmark, and tune system performance to guarantee high availability, data integrity, and pipeline reliability .
Requirement Analysis: Analyze business logic requirements to develop data specifications, schemas, and optimal data models .
Downstream Enablement: Clean, structure, and interpret large data sets to create optimized feature stores and datasets that power downstream decision-making and reporting .

Job Requirements

Education: Bachelor's degree in Computer Science, Information Technology, or a related quantitative field .
Experience: Minimum 5 years of experience in a Data Science, Big Data Analyst, or Data Infrastructure role .
Core Technical Stack: Advanced expertise in Python, PySpark, SQL, and managing massive distributed datasets .
Data Management: Proven experience in data modeling, database schema design, and process mapping .
Analytical Skills: Strong problem-solving capabilities with a deep focus on data quality, reconciliation, and automated validation frameworks .
Tools (Preferred): Familiarity with orchestration tools (e.g., Airflow), version control (Git), and data visualization tools like Tableau .

Location:

Honhui A-12F

Job:

Analytics

Schedule:

Regular

Employee Status:

Full time

AVP, Data Scientist - Platform, Data Chapter