
Global financial services firm
Data Scientist lead - Semantic Layer - Ontology/Knowledge Engineer
As an Ontology and Knowledge Graph Engineer in Chase's Data and Analytics Office, you will curate the semantic data assets that connect our enterprise data estate to a shared, intelligent knowledge graph. You will work at the intersection of formal knowledge representation, logical data modeling, and data integration, building the ontologies and mapping assets that make our data semantically interoperable across use cases. Our team values precision, intellectual curiosity, and a deep commitment to making data meaningful - and you will find that culture reflected in everything we build together. This role offers an opportunity to contribute to a foundational capability that underpins enterprise AI, analytics, and data governance at one of the world's most influential financial institutions.
Job Responsibilities
- Author Logical Data Model Ontologies that compose concepts from Upper Ontologies and Semantic Taxonomies to accurately represent how enterprise data is materialized across our data estate
- Design and maintain Knowledge Graph Mapping assets that connect relational databases, REST APIs, in-memory data structures, and real-time streaming sources to a coherent enterprise knowledge graph
- Curate Semantic Taxonomy structures using controlled vocabularies and Concept Schemes to organize enterprise concepts consistently across multiple business domains
- Contribute to the design and governance of Upper Ontologies and Semantic Taxonomies that provide a shared, standardized conceptual backbone across enterprise semantic use cases
- Enable Virtual Knowledge Graph capabilities by ensuring mapping assets and ontology definitions support on-the-fly knowledge graph materialization without physical data movement
- Engage with data architects, domain subject matter experts, AI engineers, and machine learning engineers to align ontology and mapping design decisions with both physical data structures and downstream Reasoning and Semantic Validation requirements
- Participate in ontology governance activities including versioning, change management, deprecation policies, and cross-domain alignment reviews
- Translate complex business and data requirements into formal semantic representations that are technically rigorous and accessible to non-technical stakeholders.
Required Qualifications, Capabilities, and Skills
- 3 years of experience working with semantic web technologies, knowledge graph engineering, ontology development, or linked data systems in a professional or research setting
- Demonstrated understanding of formal knowledge representation principles, including class hierarchies, property definitions, and logical constraints
- Familiarity with data mapping concepts that connect structured and semi-structured data sources to ontology-defined target vocabularies
- Working knowledge of semantic data model layers, including foundational data models, schema definition languages, and controlled vocabulary organization standards
- Exposure to relational databases and semi-structured data sources, including REST APIs, in-memory structures, and streaming data pipelines
- Ability to translate business and data requirements into formal semantic models in collaboration with data architects, domain experts, and engineering teams
- Awareness of Virtual Knowledge Graph concepts and the principles of connecting heterogeneous data sources to a shared semantic layer without physical data movement.
Preferred Qualifications, Capabilities, and Skills
- Hands-on experience authoring ontologies and knowledge graph mapping assets in a production enterprise environment
- Experience contributing to enterprise-scale knowledge graph programs within a large, complex organization
- Familiarity with Reasoning and Semantic Validation frameworks used to enforce syntactic and semantic correctness of ontology-defined concepts
- Exposure to Upper Ontology design patterns and their role in standardizing conceptual overlaps across multiple enterprise use cases
- Experience communicating complex semantic modeling decisions to non-technical stakeholders, including business analysts and product owners
- Familiarity with real-time data streaming platforms and their integration into knowledge graph mapping pipelines
- Experience contributing to ontology governance programs, including versioning strategies and cross-domain alignment reviews
ABOUT US
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans:
ABOUT THE TEAM
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.
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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
最新动态
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