National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Strategy Analytics Director

J.P. Morgan
London
1 week ago
Applications closed

Related Jobs

View all jobs

Data Analytics Director

Register Your Interest - Data Analytics Director

Assistant Vice President, EIA Data Analytics

Tax Data Analytics Director

Director of Data Analytics and AI

Head of Digital Transformation and Data Strategy

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


#J-18808-Ljbffr

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Get a Better Data Science Job After a Lay-Off or Redundancy

Redundancy can be tough to face, especially in a competitive field like data science. But it’s important to know: your experience, analytical thinking, and modelling skills are still in demand. Across sectors like healthcare, finance, e-commerce, government and AI startups, UK employers continue to seek data scientists who can deliver value through insight, prediction, and automation. This guide will walk you through how to bounce back from redundancy with purpose and clarity—whether you're a data analyst looking to step up, a mid-level data scientist, or a machine learning specialist seeking a better-aligned opportunity.

Data Science Jobs Salary Calculator 2025: Find Out What You Should Earn in the UK

Why last year’s pay survey is already out of date for UK data scientists “Am I being paid enough?” Every data professional eventually asks that question—often after a teammate reveals a hefty counter‑offer, a recruiter shares a six‑figure opening, or a headline trumpets the latest multimillion‑pound AI investment. Yet salary guides published even twelve months ago belong in a museum. Generative‑AI hype re‑priced Machine‑Learning Engineer roles, LLM fine‑tuning turned Prompt Engineering into a genuine career path, & fresh regulation forced companies to hire Responsible‑AI Officers on senior‑scientist money. To cut through the noise, DataScience‑Jobs.co.uk distilled a transparent, three‑factor formula. Insert your role, your region, & your seniority, and you’ll get a realistic 2025 salary benchmark—no stale averages, no vague ranges. This article walks you through the formula, examines the forces pushing data‑science pay ever higher, and offers five concrete actions to boost your market value within ninety days.

How to Present Data Science Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

The ability to communicate clearly is now just as important as knowing how to build a predictive model or fine-tune a neural network. In fact, many UK data science job interviews are now designed to test your ability to explain your work to non-technical audiences—not just your technical competence. Whether you’re applying for your first data science role or moving into a lead or consultancy position, this guide will show you how to structure your presentation, simplify technical content, design effective visuals, and confidently answer stakeholder questions.