
Global financial services firm
Home Lending Data & Analytics – Applied AI/ML Engineer - VP
必备技能
Python
Are you ready to play a pivotal role in shaping the future of Data & Analytics?
As a valued member of our Home Lending Data & Analytics Applied AI/ML team, you will be at the forefront of our firm-wide initiatives to innovate and enhance Data & Analytics functions. Your contributions will be instrumental in transforming the processes and tools for data, analysis, and modeling.
As an Applied AI/ML Engineer within our Home Lending Data & Analytics team, you will work in close coordination with various teams in Data & Analytics, including Data Owners, Reporting, Analysis, and Modeling. Your role will involve designing and implementing AI-powered solutions that boost operational efficiency, improve employee experience, and improve data quality. Leveraging your expertise in artificial intelligence and data analytics, you will develop state-of-the-art applications that simplify processes and yield tangible business outcomes. Your valuable contributions will play a pivotal role in transforming our way of working, ensuring we remain at the cutting edge of the industry.
Job Responsibilities:
- Write and deliver high-quality code for AI-driven Home Lending Data & Analytics initiatives, ensuring robust and efficient solutions that meet business objectives
- Work closely with Data Owners, Reporting, Analysis, and Modeling teams to address and solve complex problems including AI, ML, and advanced analytics, with a focus on generative AI technologies
- Deploy and scale AI models to production environments, ensuring robustness, reliability, security, and compliance
- Fine-tune AI models for optimal performance and efficiency, addressing any issues that arise
- Work closely with data scientists, software engineers, and product teams to integrate AI solutions into existing systems and workflows
- Own and manage the entire code lifecycle, from development through production deployment and ongoing maintenance, adhering to best practices
- Develop and maintain comprehensive technical documentation for all AI solutions, ensuring clarity, accuracy, and accessibility for both technical and non-technical stakeholders
- Continuously monitor AI performance, making necessary adjustments to maintain and improve accuracy and efficiency
- Engage with stakeholders to develop, demonstrate, and iterate on deliverables, ensuring alignment with analyst needs and regulatory requirement
Required qualifications, capabilities, and Skills:
- 7+ years of work experience.
- Masters degree in a quantitative field such as Data Science, Computer Science, Engineering, Math, Physics, etc
- Proven track record of delivering projects in the AI/ML and data analytics space
- Proficiency in Python.
- Experience with foundation LLMs (e.g., GPT-5, Claude), RAG, Agents.
- Strong understanding of software engineering principles and practices, including version control, testing, and code optimization
- Technical documentation experience preferred.
Preferred qualifications, capabilities and skills:
- Experience with AWS Cloud Platform and Databricks for deploying and managing machine learning models preferred.
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关于JPMorgan Chase

JPMorgan Chase
PublicJPMorgan Chase & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.
300,000+
员工数
New York City
总部位置
$500B
企业估值
评价
10条评价
3.8
10条评价
工作生活平衡
3.5
薪酬
4.0
企业文化
3.8
职业发展
3.2
管理层
2.8
68%
推荐率
优点
Good benefits and compensation
Supportive colleagues and environment
Flexible work arrangements
缺点
Long hours and heavy workload
Management issues and lack of direction
High stress and expectations
薪资范围
44个数据点
Mid/L4
Senior/L5
Mid/L4 · Applied AI ML Associate
2份报告
$188,500
年薪总额
基本工资
$145,000
股票
-
奖金
-
$182,000
$195,000
面试评价
4条评价
难度
3.0
/ 5
时长
14-28周
录用率
50%
体验
正面 25%
中性 75%
负面 0%
面试流程
1
Application Review
2
HR Screen
3
Hiring Manager Interview
4
In-person/Final Interview
5
Offer
常见问题
Behavioral/STAR
Past Experience
Culture Fit
Financial Knowledge
Case Study
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