Data Domain Architect Vice President

We are an intellectually diverse team of economists, statisticians, engineers, and other analytics professionals, focused on quantitative modeling within Community and Consumer Banking (CCB) at JPMorgan Chase & Co.

The team answers complex and unique questions, utilizing cutting edge quantitative and computational techniques and leveraging one of the world's largest repositories of consumer data.

We work closely with our partners throughout JPMorgan Chase to assess and execute critical business decisions.

As a Data Domain Architect - Vice President in the Finance Decision Optimization group, you will actively collaborate with various stakeholders and functional teams to determine data and model requirements for constructing data pipelines and compiling complex predictive and optimization routines into executable Python packages for prototype QA testing and production deployment.

This role provides an opportunity to apply your data science, data engineering, and Python machine learning application development skills in a dynamic and competitive environment.

Our team values precision and effective execution, and we look forward to your valuable contributions.

Job responsibilities


* Build and compile data pipelines, complex predictive models and optimization routines into executable packages for prototype QA testing and production deployment: This involves creating data processing systems, integrating predictive models, and packaging them for testing and deployment.

This requires knowledge of data science, data engineering, machine learning, and software development.


* Assist in solution back testing exercises for Fair Lending and other key stakeholders: This involves testing predictive models using historical data to see how well the model would have performed in the past.

This is often done in financial contexts to ensure models are robust and reliable.


* Conduct transaction data analyses with big data technologies on Cloud platforms and turn massive amounts of data into actionable insights that drive business value: This involves analyzing large datasets using big data technologies like Spark on AWS EMR, Databricks, and Snowflake in the cloud, and transforming vast volumes of data into actionable insights that can inform business decisions.


* Find opportunities to create and automate repeatable analysis for the business: This involves identifying areas where automated data analysis can improve efficiency and provide consistent, reliable insights.


* Work on cross functional teams and collaborate with internal and external stakeholders: This involves working with different teams within the organization and coordinating with external partners to achieve project goals.


* Proactively raise critical issues to the business and technology partners: This involves identifying potential problems or issues and bringing them to the attention of relevant stakeholders.


* Keep abreast of industry trends and provide recomme...