招聘
Machine Learning Scientist - Quant AI - Senior Associate - Machine Learning Center of Excellence

Machine Learning Scientist - Quant AI - Senior Associate - Machine Learning Center of Excellence
Palo Alto, CA, United States, US
·
On-site
·
Full-time
·
2mo ago
必备技能
Python
Java
AWS
PyTorch
TensorFlow
Azure
Machine Learning
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.
- As a Machine Learning Scientist
- Quant AI - Senior Associate within the Chief Data & Analytics Office (CDAO), you will work in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.
Job Responsibilities:
- Research, develop, and productionize high-performance machine learning and quantitative models.
- Collaborate with JPMorgan's lines of business to deliver impactful software solutions.
- Design and implement scalable data processing pipelines to optimize business results.
Required Qualifications, Capabilities, and Skills:
- PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering) with 2 years of industry experience, or MS with 5 years of experience.
- Proficient in programming languages such as C/C++, Java, or Python.
- 2 years of experience in software development and data structures.
- Deep knowledge in Machine Learning, Deep Learning, and related fields.
- Experience with major machine learning frameworks like Tensor Flow and Py Torch.
- Skilled in ETL pipelines and real-time data processing.
- Strong analytical and critical thinking skills.
- Self-motivated, with excellent communication and teamwork abilities.
Preferred Qualifications, Capabilities, and Skills:
- Experience with computational graphs and JIT compilation.
- Knowledge of hardware accelerators, GPUs, and profiling tools.
- Familiarity with cloud computing platforms (Google Cloud, AWS, Azure).
- Experience in distributed system design and development.
- Background in derivatives modeling or portfolio management.
FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.
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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
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%
推荐给朋友
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
面试经验
4次面试
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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