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J.P. Morgan's Corporate & Investment Bank (CIB) is a global leader in Banking. The world's corporations, governments and institutions entrust us with their business in more than 100 countries. The Corporate & Investment Bank supports our clients around the world providing strategic advice, raising capital and managing risk. J.P. Morgan Wholesale Payments is one of the world's largest providers of treasury management and merchant services. Wholesale Payments sits at the intersection of finance and technology and is one of the largest players (processing $6 trillion of payments a day) in an industry undergoing a major transformation. J.P. Morgan's TS business is a full-service provider of innovative cash management, trade, liquidity, commercial card, and escrow services - specifically developed to meet the challenges treasury professionals face today. More than 135,000 corporations, financial institutions, governments and municipalities in over 180 countries and territories entrust their business to J.P. Morgan. The Wholesale Payments Data and Analytics team is responsible for defining and executing the multi-year global analytics strategy and roadmap. We are a new and rapidly growing team of change agents inside J.P. Morgan. We are data scientists, design strategists, and business leaders creating and building data products that transform the way we and our clients do business - balancing the art of decision making with science. What we build matters and will have huge impact; our products and solutions will reach thousands of our clients around the world, and affect how J.P. Morgan does business, moving trillions of dollars a day. New technologies, analytics and business models are emerging with the potential to radically transform financial services. You will be at the leading edge inside the company driving change. You will: - Explore how money moves through our network and around the world turning data into insights to impact the strategy and direction of JPM products. - Apply data analytic from traditional statistics to complex machine learning techniques for a wide-ranging set of banking applications such as -but not limited to- Cash flow forecasting, Balance Attrition, Risk assessment, anti-money laundering detection, and client relationship management - Invent creative and innovative ways to answer key business questions by leveraging existing data assets or creating new ones - Develop, plan and execute analytical projects as an individual contributor and in teams. Bring order to disparate requirements with high tolerance for ambiguity, very strong problem solving ability, and excellent client engagement skills - Contribute to "productization" analytical insights in collaboration with product managers, end users, developers, and other stakeholders to integrate data discoveries and processes into operational capabilities - Be a data storyteller, deliver practical data insights in a compelling manner to senior leadership. Articulate findings clearly and concisely including presentations, discussions and visualizations. - Develop deep subject matter expertise in the wide range domains in payments and liquidity to support a diverse set of products. . Developing bank data into a data asset will require deep expertise in current-state systems and the ability to understand how it all works will allow us to insert tactical and strategic product solutions in the mix between legacy back-end. Qualifications: - Quantitativebackground –Advanced Degree in analytical field (e.g. econometrics, statistics,engineering, mathematics, computer science). 3+ year's relevant experience. Expertisein data mining, quantitative research techniques, theories, principles, andpractices. - Deepunderstanding of statistics, optimization and machine learning methodologies. Machine learning techniques will includefamiliarity or knowledge of at least one of the following areas: time seriesanalysis, supervised learning, pattern detection, natural language, maximum entropymodels and neural networks. - Trackrecord of speedily and rigorously developing and deploying user-facing machinelearning models to resolve industry problems. Excellent Python and SQL programming skills; familiarity with standarddata science tooling; understanding of algorithms and software engineeringfundamentals. - Passionate,creative, and analytical. You love data, curious to explore and unveil insightsto create a business story and to impact key business decisions - Analyticalthought leader - You can define the analytical agenda for projects, frameambiguous business questions into analytical plans (e.g., assess data needs,source files, prepare data, create new features, evaluate quality, etc.), andexecute. - Cognitiveand Communications Skill - Highly articulate, built upon an underlyingfundamental clarity of thought. General ability to root out fundamental issues,bring order from chaos, synthesize elegant insights, and drive to cleardecisions. - Leadership - Primary focus of building somethingof significance. Willingness to roll up sleeves and do whatever it takes.Self-confidence, poise, and personal presence that inspires confidence inothers. Charisma, gravitas, intellect, flexibility, and integrity thatmotivates others to trust, collaborate, and follow