The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is tasked with accelerating the firm's data and analytics journey, ensuring data quality, integrity, and security, and leveraging it to promote decision-making. The CDAO harnesses artificial intelligence and machine learning technologies to support the firm's commercial goals, develop new products, and enhance risk management. The Strategy and Execution team defines and executes the CDO vision and strategy. The Firmwide Chief Data Office (CDO) maximizes the value and impact of data globally, with teams focused on data strategy, impact optimization, privacy, governance, transformation, and talent.
As a Data Strategy Analytics Manager within JP Morgan Chase, you will be responsible for developing insights and tracking mechanisms to support the execution of our Firmwide Data Strategy. Your role will involve working across various systems and functions to identify necessary data, design analyses and reports, and synthesize insights to inform our senior leadership about the progress and opportunities presented by our data strategy. You will be part of a team of skilled data scientists who promote decision-making through insight. Your hands-on approach will span a wide range of systems, from Cloud to on-premise, applying statistical rigor and advanced data science methods to your work. In collaboration with our platform partners, you will design observability across a broad range of processes and build instrumentation to fill data gaps.
Job responsibilities:
- Actively develops thorough understanding of complex business problems and processes related to aspects of our data strategy
- Collaborates with business partners to understand their systems and processes
- Leads tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
- Generates structured and meaningful insights from data analysis and modelling exercise about critical strategic initiatives, and present them in appropriate format according to the audience.
- Provides mentorship and oversight for junior data scientists to build a collaborative working culture.
- Partnesr with machine learning engineers to deploy machine learning solutions.
- Owns key model maintenance tasks and lead remediation actions as needed.
- Stays informed about the latest trends in the AI/ML/LLM/GenAI research and operate with a continuous-improvement mindset.
Required qualifications, capabilities, and skills:
- Advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics) or equivalent experience
- Extensive relevant experience in data analysis and AI / ML domain
- In-depth expertise and extensive experience with ML projects, both supervised and unsupervised
- Strong programming skills with Python, R, or other equivalent languages.
- Proficient in working with large datasets and handling complex data issues.
- Experience with broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, XGBoost, graph analytics, and neural nets.
- Excellent solution ideation, problem solving, communication (verbal and written), and teamwork skills.
Preferred qualifications, capabilities, and skills:
- Familiarity with machine learning engineering and developing/implementing machine learning models within AWS or other cloud platforms.
- Familiarity with the financial services industry.
- Experience building or managing datasets for telemetry and observability of analytic and data systems
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