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Head of Data Strategy & Commercialization – Executive Director, APAC Chief Data & Analytics Office

JPMorgan Chase

Head of Data Strategy & Commercialization – Executive Director, APAC Chief Data & Analytics Office

JPMorgan Chase

Singapore, SG

·

On-site

·

Full-time

·

1mo ago

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is pivotal in advancing the firm's data and analytics capabilities, ensuring strong adherence to data & AI risk & control while enabling the data & analytics strategy for superior decision-making and business outcomes to serve our clients and markets. By leveraging data & AI/ML, the CDAO develops innovative solutions to support commercial goals, enhance productivity, and manage risks. The Asia Pacific Chief Data & Analytics Office (CDAO) advances the firm’s data and analytics strategy, platforms, solutions, capabilities, and governance to deliver trustworthy, responsible, innovative, and commercially value outcomes across the APAC markets and businesses.

The Head of Data Strategy and Commercialization, APAC will establish firmwide approaches to adopting our data strategy, focusing on designating data authority from source and making data available for a wide range of uses. This leader will interact with teams across the firm, including lines of business and corporate functions, and play an instrumental role in planning, collaborating, and executing complex data strategy initiatives. It is critical to communicate effectively with leadership and stakeholders, guide team members, and take ownership of data strategy concepts that impact end user products.

Key Responsibilities:

  • Define and execute the APAC data strategy and roadmap, ensuring alignment with global and regional priorities to support business growth, business process innovation, and operational efficiency.

  • Collaborate with business stakeholders to identify and prioritize the availability and usage of critical data assets as data products to enable high-value use cases and opportunities across business lines and functions.

  • Work with business units to understand data needs, translate them into technical requirements, and deliver actionable data solutions.

  • Ensure the implementation and adoption of robust data management frameworks covering data architecture, data modeling, metadata management, data lineage, and data cataloging

  • Collaborate with engineering and analytics teams to build scalable data pipelines, data lakes, and data warehouses for effective storage, processing, and retrieval.

  • Lead cross-functional teams to deliver data projects from concept to launch, ensuring technical feasibility and business value.

  • Oversee the full lifecycle of strategic data initiatives, from concept and experimentation to production deployment and adoption.

  • Act as a trusted advisor on data strategy and execution for senior stakeholders, ensuring consistent messaging and coordination across internal teams.

  • Analyze market trends and customer needs to inform data product development and commercialization strategies.

  • Build and maintain strong relationships with internal and external stakeholders, including technology, business, risk, and compliance teams.

  • Track performance of data products and initiatives, providing insights and recommendations for improvement using advanced analytics and reporting tools.

  • Stay updated on industry best practices, emerging technologies, and regulatory changes related to data management and data products.

Required Qualifications, Capabilities, and Skills

  • Bachelor’s in Computer Science, Artificial Intelligence, Applied Statistics, Mathematics, or related quantitative field.

  • Formal training or certification on experience in AI/ML solution development, delivery, and technical leadership within financial services and 15+ years of experience in a similar role

  • Experience working in data strategy & analysis, from collection and cleaning to analysis and interpretation, ensuring data quality and making data-driven decisions.

  • Experience engaging with regulators and participating in regulatory consultations.

  • Experience with data governance frameworks, processes, and regulatory requirements.

  • Experience collaborating with cross-functional teams across regions and building scalable capabilities.

  • Ability to handle ambiguity and uncertainty, navigate complex situations, and make informed decisions.

  • Understanding of APAC regulatory nuances and managing the regulatory landscape in a global organization.

  • Exceptional written and verbal communication skills, with the ability to articulate strategic direction and technical concepts to diverse audiences.

  • Strategic thinker with a track record of developing innovative solutions.

Preferred Qualifications, Capabilities, and Skills

  • Experience in the financial services industry, particularly in APAC.

  • Prior experience working with or managing globally distributed technical teams.

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About JPMorgan Chase

JPMorgan Chase

JPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.

300,000+

Employees

New York City

Headquarters

Reviews

4.2

10 reviews

Work Life Balance

4.2

Compensation

4.3

Culture

4.5

Career

4.4

Management

4.1

75%

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Pros

Good pay and benefits

Work-life balance

Career advancement opportunities

Cons

Heavy workload at times

Career advancement takes time

Pay could be better in some roles

Salary Ranges

47 data points

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2 reports

$188,500

total / year

Base

$145,000

Stock

-

Bonus

-

$182,000

$195,000

Interview Experience

4 interviews

Difficulty

2.8

/ 5

Duration

14-28 weeks

Interview Process

1

Application Review

2

HireVue Video Interview

3

Technical/Behavioral Assessment

4

Final Interview Round

5

Offer Decision

Common Questions

Behavioral/STAR

Technical Knowledge

Past Experience

Culture Fit

Case Study