📍 Job Overview

Job Title: Principal Platform Engineer, Assistant Vice President
Company: State Street
Location: Hangzhou, Zhejiang, China
Job Type: Full-Time, On-site
Category: Data Science & AI
Date Posted: 2025-06-26
Experience Level: 5-10 years

🚀 Role Summary

Lead a small agile team to deliver highly effective AI solutions, collaborating with global teams to execute on global projects and improve AI best practices.
Adopt modern AI tools to design and develop AI solutions, disrupting traditional AI development approaches and improving overall productivity.
Develop, train, and mentor junior data scientists, fostering a high-performance team culture.
Become a subject matter expert in modern AI tools, foundational AI model design, development, and validation.
Integrate, customize, and train known AI models and algorithms in open-source libraries to solve financial market problems and perform validation and bug fixes.
Drive model rationalization, reusability, scalability, and implement modern MLOps and model observability.

📝 Enhancement Note: This role requires a strong balance of technical expertise in AI and strong team collaboration skills to lead a small agile team and deliver effective AI solutions. The candidate should have rich experience in AI application development and be able to adapt to fast-changing business and client needs.

💻 Primary Responsibilities

Solution Delivery: Lead a small agile team to deliver highly effective AI solutions tailored to fast-changing business and client needs. Collaborate with global teams to execute on global projects and keep improving model solutions and AI best practices.
AI Tool Adoption: Adopt various modern AI tools to design and develop AI solutions, disrupting traditional AI development approaches and improving overall productivity.
Team Development: Develop, train, and mentor junior data scientists, ensuring their continuous growth and development, and fostering a high-performance team culture.
AI Expertise: Become a subject matter expert in modern AI tools, foundational AI model design, development, and validation by converting financial data and models to mathematical and computer science level design and implementation.
Model Development: Integrate, customize, and train known AI models and algorithms in open-source libraries to solve financial market problems and perform validation and bug fixes.
MLOps & Model Observability: Drive model rationalization, reusability, scalability, and implement modern MLOps and model observability to ensure efficient and reliable AI solutions.
Model Specification: Write model specification documents outlining problem statements, assumptions, data inputs, methodologies, implementation frameworks, and test results. Work with external model validation groups and implement changes for go-live.
IT Integration & Deployment: Support IT integration, QA/UAT, and deployment of AI microservices, operationalizing and productizing resulting models and AI solutions.
Production Issue Resolution: Support production issues pertinent to models and algorithms, including those used in open-source libraries if any.

🎓 Skills & Qualifications

Education:

Master’s degree required (preferably in computer science, financial mathematics, or financial engineering), PhD preferred.

Experience:

8+ years of hands-on coding experience with foundational AI algorithms and familiarity with deep learning neural networks, such as various supervised and unsupervised models and reinforcement learning.
8+ years of modern, object-oriented or functional programming and design experience (Python, Java) and proficiency in AI code generation tools like GitHub Co-pilot/Cursor.

Required Skills:

Proficient in public cloud development environments and native products such as Azure ML, Azure AI Foundry, Snowflake, Databricks Mosaic, or AWS Bedrock/Sagemaker.
Familiar with major financial instruments, reference data, market data, investment, and risk management concepts.
Fluent in English and Mandarin; excellent written and verbal communication skills at all stakeholder levels across multiple countries.
Result-driven, detail-oriented, and candid attitude.

Preferred Skills:

Prior data scientist role in a financial institution and model validation experience.
Prior risk and investment management experience in the asset management industry.
Structured and unstructured data management tools (Snowflake, Databricks, PostgreSQL, Hadoop, etc.).
Passed Chartered Financial Analyst (CFA) level II or equivalent.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A diverse portfolio showcasing various AI projects, including financial market problem-solving, model development, and AI solution delivery.
Live demonstrations of AI solutions and models, highlighting their effectiveness and user impact.
Case studies detailing problem statements, methodologies, implementation frameworks, and test results.

Technical Documentation:

Detailed documentation of AI models, including data inputs, assumptions, and validation processes.
Code comments and documentation adhering to best practices and industry standards.
Version control, deployment processes, and server configuration documentation.

📝 Enhancement Note: Given the role's focus on AI solution delivery and team leadership, the candidate's portfolio should emphasize collaborative projects, model development, and AI solution implementation. The portfolio should also demonstrate the candidate's ability to adapt to fast-changing business needs and work effectively with global teams.

💵 Compensation & Benefits

Salary Range: The salary range for this role in Hangzhou, China, is estimated to be between 700,000 - 1,200,000 RMB per year, based on market research and the candidate's experience level. This estimate takes into account the cost of living in Hangzhou and the role's requirements for AI expertise and team leadership.

Benefits:

Medical care, insurance, and savings plans
Flexible work programs
Development programs and educational support
Inclusion, diversity, and social responsibility initiatives

Working Hours: The role requires a standard full-time work arrangement, with the possibility of flexible work programs. The working hours may vary depending on project deadlines and maintenance windows.

📝 Enhancement Note: The salary range provided is an estimate based on market research and the role's requirements. Actual salary offers may vary depending on the candidate's qualifications and the company's internal compensation structure.

🎯 Team & Company Context

Company Culture:

Industry: State Street is one of the largest custodian banks, asset managers, and asset intelligence companies in the world, focusing on technology, product innovation, and financial services.
Company Size: State Street has a large global presence, with over 39,000 employees across 28 countries. This size allows for extensive resources and opportunities for career growth.
Founded: State Street was founded in 1792 and has a rich history in the financial services industry.

Team Structure:

The team is a small, agile group focused on delivering effective AI solutions and collaborating with global teams to execute on global projects.
The team works closely with State Street affiliates, Global Technology Services (GTS), and other third-party vendors within the SSGA ecosystem globally.
The team operates under a federated operating model to deliver against a target end state AI ecosystem blueprint and adopts GTS AI-related technology, risk, security, and data governance standards.

Development Methodology:

The team follows Agile methodologies, with a focus on delivering high-quality AI solutions tailored to fast-changing business and client needs.
The team emphasizes collaboration, continuous improvement, and efficient project execution.
The team works closely with GTS to ensure that AI solutions align with the company's architectural standards and best practices.

Company Website: State Street

📝 Enhancement Note: State Street's global presence and extensive resources provide ample opportunities for career growth and collaboration with diverse teams. The company's focus on technology and innovation makes it an attractive choice for AI professionals seeking to work in a dynamic and challenging environment.

📈 Career & Growth Analysis

AI Career Level: This role is a principal data scientist position, focusing on AI solution delivery, team leadership, and collaboration with global teams. The candidate will be responsible for leading a small agile team and delivering effective AI solutions tailored to fast-changing business and client needs.

Reporting Structure: The candidate will report directly to the Global Head of Data, Analytics & AI Services (GHDAAIS) and work closely with State Street affiliates, Global Technology Services (GTS), and other third-party vendors within the SSGA ecosystem globally.

Technical Impact: The candidate will have a significant impact on the delivery of AI technology services provided to the Global Advisors business at State Street. The role will function as the AI specialist and solution lead for AI-related services, working closely with GTS and other vendors to ensure that AI solutions align with the company's architectural standards and best practices.

Growth Opportunities:

Technical Skill Development: The role offers opportunities for the candidate to develop their expertise in modern AI tools, foundational AI model design, development, and validation. The candidate will also have the chance to stay up-to-date with the latest trends in AI and financial market problem-solving.
Team Leadership: The role provides opportunities for the candidate to develop their leadership skills by leading a small agile team and collaborating with global teams to execute on global projects. The candidate will also have the chance to mentor junior data scientists and foster a high-performance team culture.
Architecture Decision-Making: The role offers opportunities for the candidate to participate in architecture decision-making processes and contribute to the development of the company's AI ecosystem blueprint.

📝 Enhancement Note: This role offers significant growth opportunities for AI professionals seeking to develop their expertise in AI solution delivery, team leadership, and collaboration with global teams. The role's focus on fast-changing business needs and global project execution provides a dynamic and challenging environment for career growth.

🌐 Work Environment

Office Type: State Street's Hangzhou office is a modern, collaborative workspace designed to facilitate team interaction and knowledge sharing. The office is equipped with state-of-the-art technology and tools to support AI development and solution delivery.

Office Location(s): Hangzhou, Zhejiang, China

Workspace Context:

Collaboration: The office encourages cross-functional collaboration between data scientists, engineers, and other teams to ensure effective AI solution delivery and continuous improvement.
Development Tools: The office provides access to modern AI tools, programming languages, and development environments to support AI development and solution delivery.
Team Interaction: The office fosters a culture of knowledge sharing, technical mentoring, and continuous learning, with regular team meetings and workshops to discuss AI best practices and project progress.

Work Schedule: The role requires a standard full-time work arrangement, with the possibility of flexible work programs. The working hours may vary depending on project deadlines and maintenance windows.

📝 Enhancement Note: State Street's Hangzhou office provides a modern, collaborative workspace designed to support AI development and solution delivery. The office's focus on cross-functional collaboration, knowledge sharing, and continuous learning makes it an ideal environment for AI professionals seeking to grow their careers in a dynamic and challenging environment.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: The interview process will include a technical assessment focused on AI algorithm development, model validation, and AI solution delivery. The candidate will be expected to demonstrate their expertise in modern AI tools, foundational AI model design, development, and validation.
Team Collaboration: The interview process will also assess the candidate's ability to work effectively with global teams and collaborate with stakeholders across multiple countries. The candidate will be expected to demonstrate strong communication skills and the ability to adapt to fast-changing business needs.
AI Solution Delivery: The interview process will evaluate the candidate's ability to deliver effective AI solutions tailored to fast-changing business and client needs. The candidate will be expected to provide examples of AI projects they have led and the impact they have had on business outcomes.
Final Evaluation: The final evaluation will focus on the candidate's technical expertise, team collaboration skills, and alignment with State Street's AI ecosystem blueprint and architectural standards.

Portfolio Review Tips:

AI Project Selection: The candidate should select AI projects that demonstrate their expertise in AI algorithm development, model validation, and AI solution delivery. The projects should highlight the candidate's ability to adapt to fast-changing business needs and work effectively with global teams.
Case Study Structure: The candidate should structure their portfolio case studies to include problem statements, methodologies, implementation frameworks, and test results. The case studies should also highlight the candidate's ability to deliver effective AI solutions tailored to fast-changing business and client needs.
Code Quality Demonstration: The candidate should demonstrate their proficiency in modern AI tools, programming languages, and development environments. The candidate should also highlight their ability to write clean, efficient, and well-documented code.
AI Solution Demonstration: The candidate should demonstrate their ability to deliver effective AI solutions tailored to fast-changing business and client needs. The candidate should provide live demonstrations of AI solutions and models, highlighting their effectiveness and user impact.

Technical Challenge Preparation:

AI Algorithm Development: The candidate should brush up on their AI algorithm development skills, focusing on foundational AI algorithms, deep learning neural networks, and reinforcement learning. The candidate should also familiarize themselves with modern AI tools and AI code generation tools like GitHub Co-pilot/Cursor.
Model Validation: The candidate should review model validation techniques and best practices, ensuring they are up-to-date with the latest trends in AI model validation. The candidate should also familiarize themselves with external model validation groups and their processes.
AI Solution Delivery: The candidate should review AI solution delivery best practices and ensure they are familiar with Agile methodologies, IT integration, QA/UAT, and deployment processes. The candidate should also brush up on their communication skills and ability to work effectively with global teams.

ATS Keywords:

AI Solution Delivery
AI Algorithm Development
Model Validation
AI Team Leadership
Global Project Execution
Agile Methodologies
IT Integration
QA/UAT
Deployment Processes
Cloud Development
Public Cloud Development Environments
Azure ML
Azure AI Foundry
Snowflake
Databricks Mosaic
AWS Bedrock/Sagemaker
Financial Instruments
Risk Management
Portfolio Management
Data Governance
AI Code Generation Tools
GitHub Co-pilot/Cursor
Team Collaboration
Stakeholder Management
Communication Skills
English
Mandarin

📝 Enhancement Note: The interview process for this role will focus on the candidate's technical expertise in AI algorithm development, model validation, and AI solution delivery. The candidate should be prepared to demonstrate their ability to work effectively with global teams and adapt to fast-changing business needs. The candidate should also be familiar with State Street's AI ecosystem blueprint and architectural standards.

🛠 Technology Stack & Web Infrastructure

AI Tools & Frameworks:

Modern AI tools, such as Azure ML, Azure AI Foundry, Snowflake, Databricks Mosaic, and AWS Bedrock/Sagemaker.
AI code generation tools, such as GitHub Co-pilot/Cursor.
Deep learning neural networks, such as various supervised and unsupervised models and reinforcement learning.
Programming languages, such as Python and Java.

AI Development Environments:

Public cloud development environments, such as Azure, AWS, or Google Cloud.
Modern, object-oriented or functional programming and design environments.

AI Deployment & Infrastructure:

IT integration, QA/UAT, and deployment processes.
Server configuration and management.
Containerization and orchestration tools, such as Docker and Kubernetes.
Infrastructure as Code (IaC) tools, such as Terraform or CloudFormation.

📝 Enhancement Note: The technology stack for this role includes modern AI tools, deep learning neural networks, and programming languages. The candidate should be familiar with public cloud development environments, AI code generation tools, and AI deployment and infrastructure processes. The candidate should also have experience with IT integration, QA/UAT, and deployment processes.

👥 Team Culture & Values

AI Development Values:

Innovation: State Street values innovation in AI development and solution delivery. The company encourages its AI professionals to stay up-to-date with the latest trends in AI and financial market problem-solving.
Collaboration: State Street fosters a culture of collaboration between AI professionals, engineers, and other teams. The company encourages knowledge sharing, technical mentoring, and continuous learning.
Quality: State Street is committed to delivering high-quality AI solutions tailored to fast-changing business and client needs. The company encourages its AI professionals to adhere to best practices in AI algorithm development, model validation, and AI solution delivery.
Performance: State Street focuses on delivering AI solutions that drive business outcomes and improve user experiences. The company encourages its AI professionals to measure the impact of their AI solutions and continuously improve their performance.

Collaboration Style:

Cross-Functional Collaboration: State Street encourages collaboration between AI professionals, engineers, and other teams to ensure effective AI solution delivery and continuous improvement. The company provides a modern, collaborative workspace designed to facilitate team interaction and knowledge sharing.
Code Review Culture: State Street fosters a culture of code review and peer programming to ensure high-quality AI solutions and continuous learning. The company encourages its AI professionals to review each other's code and provide constructive feedback.
Knowledge Sharing: State Street encourages knowledge sharing, technical mentoring, and continuous learning. The company provides regular team meetings and workshops to discuss AI best practices and project progress.

📝 Enhancement Note: State Street's AI development values emphasize innovation, collaboration, quality, and performance. The company fosters a culture of collaboration between AI professionals, engineers, and other teams, with a focus on knowledge sharing, technical mentoring, and continuous learning. The company's commitment to delivering high-quality AI solutions tailored to fast-changing business and client needs makes it an attractive choice for AI professionals seeking to work in a dynamic and challenging environment.

⚡ Challenges & Growth Opportunities

Technical Challenges:

AI Algorithm Development: The candidate may face technical challenges in AI algorithm development, model validation, and AI solution delivery. The candidate should be prepared to adapt to fast-changing business needs and work effectively with global teams.
AI Solution Delivery: The candidate may face challenges in delivering effective AI solutions tailored to fast-changing business and client needs. The candidate should be prepared to work effectively with stakeholders across multiple countries and adapt to the company's AI ecosystem blueprint and architectural standards.
AI Tool Adoption: The candidate may face challenges in adopting modern AI tools, AI code generation tools, and public cloud development environments. The candidate should be prepared to stay up-to-date with the latest trends in AI tools and development environments.

Learning & Development Opportunities:

Technical Skill Development: The role offers opportunities for the candidate to develop their expertise in modern AI tools, foundational AI model design, development, and validation. The candidate will also have the chance to stay up-to-date with the latest trends in AI and financial market problem-solving.
Team Leadership: The role provides opportunities for the candidate to develop their leadership skills by leading a small agile team and collaborating with global teams to execute on global projects. The candidate will also have the chance to mentor junior data scientists and foster a high-performance team culture.
Architecture Decision-Making: The role offers opportunities for the candidate to participate in architecture decision-making processes and contribute to the development of the company's AI ecosystem blueprint.

📝 Enhancement Note: This role offers significant technical challenges and growth opportunities for AI professionals seeking to develop their expertise in AI solution delivery, team leadership, and collaboration with global teams. The role's focus on fast-changing business needs and global project execution provides a dynamic and challenging environment for career growth.

💡 Interview Preparation

Technical Questions:

AI Algorithm Development: The interviewer may ask questions about the candidate's expertise in AI algorithm development, model validation, and AI solution delivery. The candidate should be prepared to discuss their experience with modern AI tools, deep learning neural networks, and reinforcement learning. The candidate should also be familiar with AI code generation tools like GitHub Co-pilot/Cursor.
AI Solution Delivery: The interviewer may ask questions about the candidate's ability to deliver effective AI solutions tailored to fast-changing business and client needs. The candidate should be prepared to discuss their experience with Agile methodologies, IT integration, QA/UAT, and deployment processes. The candidate should also be familiar with State Street's AI ecosystem blueprint and architectural standards.
AI Tool Adoption: The interviewer may ask questions about the candidate's experience with modern AI tools, AI code generation tools, and public cloud development environments. The candidate should be prepared to discuss their familiarity with Azure ML, Azure AI Foundry, Snowflake, Databricks Mosaic, and AWS Bedrock/Sagemaker.

Company & Culture Questions:

AI Development Values: The interviewer may ask questions about the candidate's understanding of State Street's AI development values, including innovation, collaboration, quality, and performance. The candidate should be prepared to discuss their alignment with these values and how they have demonstrated them in previous roles.
Collaboration Style: The interviewer may ask questions about the candidate's experience with cross-functional collaboration, code review culture, and knowledge sharing. The candidate should be prepared to discuss their ability to work effectively with global teams and adapt to fast-changing business needs.
AI Solution Delivery: The interviewer may ask questions about the candidate's ability to deliver effective AI solutions tailored to fast-changing business and client needs. The candidate should be prepared to discuss their experience with Agile methodologies, IT integration, QA/UAT, and deployment processes. The candidate should also be familiar with State Street's AI ecosystem blueprint and architectural standards.

Portfolio Presentation Strategy:

AI Project Selection: The candidate should select AI projects that demonstrate their expertise in AI algorithm development, model validation, and AI solution delivery. The projects should highlight the candidate's ability to adapt to fast-changing business needs and work effectively with global teams.
Case Study Structure: The candidate should structure their portfolio case studies to include problem statements, methodologies, implementation frameworks, and test results. The case studies should also highlight the candidate's ability to deliver effective AI solutions tailored to fast-changing business and client needs.
Code Quality Demonstration: The candidate should demonstrate their proficiency in modern AI tools, programming languages, and development environments. The candidate should also highlight their ability to write clean, efficient, and well-documented code.
AI Solution Demonstration: The candidate should demonstrate their ability to deliver effective AI solutions tailored to fast-changing business and client needs. The candidate should provide live demonstrations of AI solutions and models, highlighting their effectiveness and user impact.

📝 Enhancement Note: The interview process for this role will focus on the candidate's technical expertise in AI algorithm development, model validation, and AI solution delivery. The candidate should be prepared to demonstrate their ability to work effectively with global teams and adapt to fast-changing business needs. The candidate should also be familiar with State Street's AI ecosystem blueprint and architectural standards.

📌 Application Steps

To apply for this Principal Platform Engineer, Assistant Vice President role at State Street:

Portfolio Customization: Customize your portfolio to highlight your expertise in AI algorithm development, model validation, and AI solution delivery. Select AI projects that demonstrate your ability to adapt to fast-changing business needs and work effectively with global teams. Structure your portfolio case studies to include problem statements, methodologies, implementation frameworks, and test results.
Resume Optimization: Optimize your resume to highlight your technical skills, AI project experience, and leadership abilities. Include relevant keywords and phrases to improve your resume's visibility in AI-focused job searches.
Technical Interview Preparation: Brush up on your AI algorithm development skills, focusing on foundational AI algorithms, deep learning neural networks, and reinforcement learning. Review model validation techniques and best practices, ensuring you are up-to-date with the latest trends in AI model validation. Familiarize yourself with Agile methodologies, IT integration, QA/UAT, and deployment processes. Brush up on your communication skills and ability to work effectively with global teams.
Company Research: Research State Street's AI ecosystem blueprint, architectural standards, and AI development values. Familiarize yourself with the company's commitment to innovation, collaboration, quality, and performance in AI development and solution delivery. Prepare questions to ask the interviewer about the company's AI initiatives and growth opportunities.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Principal Platform Engineer, Assistant Vice President