位于c22的 5 个优质process quality engineer职位（正在招聘！）

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Description and Requirements

Job Responsibilities:

1. Strategy & Leadership

Define the AI Vision: Develop and execute a comprehensive AI roadmap aligned with the leasing business strategy, prioritizing high-impact use cases across the lease lifecycle (origination, underwriting, servicing, collections, asset management) .
Establish the CoE Framework: Build the governance, operating model, and best practices for AI adoption, including model development standards, ethical AI guidelines, data privacy protocols, and performance measurement frameworks.
Stakeholder Engagement: Partner with Commercial, Credit, Risk, Operations, and DT leaders to identify pain points and opportunities where AI can drive efficiency, revenue growth, and risk reduction.

2. Solution Development

Credit & Risk Intelligence: Lead development of AI/ML models for credit scoring, lease default prediction, and portfolio risk analytics—leveraging both traditional financial data and alternative data sources .
Process Automation: Deploy NLP and generative AI solutions to automate lease document review, contract analysis, and compliance checking; streamline RFP responses and lease negotiation workflows .
Commercial Optimization: Build predictive analytics for lease pricing optimization (NPV/NER modeling), tenant retention scoring, and market intelligence—analyzing comparable properties, pricing trends, and competitive positioning .
Servicing & Collections: Implement AI-driven customer service (chatbots, intelligent routing) and collections optimization models to improve recovery rates and customer experience.

3. Data & Technology

Data Strategy: Define data requirements, ensure data quality, and establish data pipelines to feed AI models—integrating internal systems (leasing management, CRM, ERP) with external data sources.
Technology Selection: Evaluate and select AI/ML platforms, MLOps infrastructure, and vendor solutions; oversee the build vs. buy decisions for AI capabilities.
Model Governance: Establish rigorous validation, monitoring, and retraining protocols to ensure model accuracy, fairness, and regulatory compliance.

4. Team Building & Culture

Talent Acquisition: Recruit, mentor, and lead a cross-functional team of data scientists, ML engineers, data analysts, and AI product managers.
Change Management: Champion AI adoption across the organization, delivering training programs to upskill commercial and operational teams in leveraging AI tools .

5. Innovation Culture: Foster a culture of experimentation and continuous learning, encouraging rapid prototyping and data-driven decision-making

Job Requirements:

1. Education & Experience

Bachelor's degree in Computer Science, Data Science, Engineering, Finance, or related field; Master's or PhD preferred.
10+ years of experience in data science, AI/ML, or analytics leadership roles, with 5+ years specifically in financial services or leasing/finance industries.
Proven track record of deploying AI/ML solutions in production at scale within regulated financial environments.

2. Technical Skills

Deep expertise in machine learning (supervised/unsupervised learning, deep learning, NLP, time series forecasting) and generative AI applications.
Proficiency in Python/R, SQL, and cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
Experience with MLOps practices (model CI/CD, monitoring, versioning) and ML frameworks.
Understanding of leasing economics, credit risk modeling, and financial analytics—including NPV, NER, and cash flow modeling .
Knowledge of lease accounting standards, regulatory compliance, and data privacy (GDPR, CCPA) .

3. Business & Leadership Skills

Strong commercial acumen with ability to translate complex technical concepts into business value propositions for executive stakeholders .
Experience building and leading high-performing data science teams in fast-paced environments.
Exceptional communication and influencing skills; ability to drive change across organizational silos.
Creative problem-solver who can think beyond conventional financial services approaches .

4. Key Success Metrics

Business Impact: Measurable improvements in lease origination velocity, credit loss reduction, portfolio yield optimization, and operational cost savings.
Model Performance: Accuracy, fairness, and stability of AI models; adherence to governance and compliance standards.
Adoption Rates: User adoption of AI tools across commercial, credit, and operations teams.
Innovation Pipeline: Number of new AI use cases identified and piloted annually.

Additional Locations:

China - Beijing - 北京（Beijing）

China

China - Beijing

China - Beijing - 北京（Beijing）

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