Data Strategy Analytics Director

J.P. Morgan; London; On-site; GBP 70,000 - 110,000; Full time

Job summary

A leading financial institution is seeking a Data Strategy Analytics Manager to develop insights to support their data strategy. This role requires advanced analytical skills and collaboration across various teams. Candidates should possess strong programming capabilities and significant experience in data analysis, particularly within the AI/ML domain.

Qualifications

• MS/PhD in a quantitative field required.
• Extensive experience in data analysis and AI/ML.
• Strong programming skills in Python or R.

Responsibilities

• Develops insights and tracking mechanisms for data strategy.
• Collaborates with business partners on data-related tasks.
• Mentors junior data scientists.

Skills

Data Analysis

Artificial Intelligence

Machine Learning

Programming (Python, R)

Problem Solving

Statistical Rigor

Communication

Education

Advanced degree in Data Science, Computer Science or related field

Tools

SQL

Spark

Scikit-Learn

XGBoost

Job description

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