招聘
必备技能
Python
SQL
AWS
TensorFlow
Excel
Tableau
Machine Learning
Duties: Develop frameworks to extract data from multiple sources and apply business rules to generate datasets for end-user consumption. Design and manage scalable data pipelines to process complex datasets using parallelism, multi-threading, and batch processing. Enhance pipeline performance and ensure data and logic consistency across platforms. Perform statistical analysis to interpret data, identify trends, and draw conclusions. Conduct data exploration and create visualizations to support analysis of large datasets. Define data models, metadata, and data dictionaries to support analytical exploration. Execute requirements gathering, design, development, testing, implementation, and support. Perform user acceptance testing and deliver demos to stakeholders. Identify data issues and patterns to improve system architecture and code quality. Lead development activities, communicate timelines and blockers, and coordinate with stakeholders. Mentor junior team members.
QUALIFICATIONS:
Minimum education and experience required: Master's degree in Business Analytics, Data Science, Information Technology, Computer Science, or related field of study plus 3 years (36 months) of experience in the job offered or as Quant Modeling, Data Scientist, Data Engineer, System Engineer, or related occupation. The employer will alternatively accept a Bachelor's degree in Business Analytics, Data Science, Information Technology, Computer Science, or related field of study plus 5 years (60 months) of experience in the job offered or as Quant Modeling, Data Scientist, Data Engineer, System Engineer, or related occupation.
Skills Required: This position requires (3) years of experience with the following: Developing ETL pipelines for data migration and processing using Python; Optimizing data workflows using Python libraries including Pandas and Num Py; Creating stored procedures and utilizing Windows functions using SQL. This position requires (1) year of experience with the following: Creating dashboards and reports using data visualization and business intelligence tools including Tableau; Interpreting data, identifying trends, and drawing conclusions using machine learning frameworks including scikit-learn and Tensor Flow. This position requires any amount of experience with the following: Extracting and loading data from AWS, flat files, and Oracle databases using Python and Excel; Building and optimizing data pipelines for datasets using ETL processes and data migration strategies; Modularizing code using object- oriented programming; Performing parallel processing using multi-threading; Enhancing performance using caching techniques; Managing and analyzing data using database systems and frameworks including Oracle PL/SQL and MS SQL Server; Processing and analyzing data using cloud platforms and big data technologies including AWS, Databricks, and Py Spark; Leading development activities using Agile methodologies; Tracking deliverables and resolving technical blockers using JIRA.
Job Location: 8181 Communications Pkwy, Plano, TX 75024.
Full-Time.
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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
3.2
薪酬
4.1
企业文化
3.8
职业发展
3.0
管理层
2.5
65%
推荐给朋友
优点
Good benefits and compensation
Supportive and collaborative environment
Flexible work arrangements
缺点
Long hours and heavy workload
Management issues and lack of direction
High stress during peak times
薪资范围
41个数据点
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Analytics Solutions Associate
1份报告
$139,000
年薪总额
基本工资
$107,000
股票
-
奖金
-
$139,000
$139,000
面试经验
5次面试
难度
3.0
/ 5
时长
14-28周
录用率
40%
体验
正面 20%
中性 80%
负面 0%
面试流程
1
Application Review
2
HireVue Video Interview
3
Recruiter Screen
4
Superday/Panel Interview
5
Final Interview
6
Offer
常见问题
Behavioral/STAR
Technical Knowledge
Culture Fit
Past Experience
Case Study
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