As an integral part of the Enterprise Supply Chain framework, ESC Analytics is responsible for reporting and analysis on the end-to-end supply chain operations within Citigroup on a global basis. Within that the Data Science Team works with big data technologies to develop predictive capabilities and understand supplier, business behaviors to support the ESC Functions, and various Citi Businesses that use third parties in their supply chains.

This role will focus on the research, optimization and execution of machine learning algorithms within the p2p process of the Enterprise Supply Chain and drive a deep behavioral understanding and analysis of the overall supply chain. Experience in problem solving, including developing machine learning experiments for statistical inference on complex business processes, develop deep learning architectures for pattern recognition and optimization to formulate strategic business recommendations.

Primary Responsibilities

• Statistical Analysis: Analyze integrity and structure of data sets to develop data appropriate machine learning models (Random Forest, Markov Models, Association Rules Mining, SVM, GBM, etc...) and use appropriate model evaluation techniques ( confusion matrix, ROC curve, cross validation, etc...) for algorithm development.

• Data Modeling: Design and build efficient, flexible and sustainable data and statistical models with statistical scripting language (R, Python, SAS) to run necessary machine learning experiments and optimization research within a Hadoop framework. Strong feature engineering acumen.

• Research and Production: Carry out critical research of current and future concepts of machine learning to internally experiment and drive new applicable innovative techniques within a non-traditional procurement space. Understand strengths and weakness of the data and have the ability to operate in the “grey” and make logically sound decisions with supported research

• Business Analysis: Interface with ESC business units to understand underlying business drivers for data acquisition, ETL and machine learning modeling

• Analysis: Provide analytic insights on model training, performance and enhancement, and analytics across Citi geographies and businesses to drive strategic business planning

• Visualizations: Develop comprehensive visualizations that supports and grow a data culture within the organization. Bring to the forefront process and expense opportunities to assist with strategic conversations through compelling visualizations

Qualifications:

• Strong statistical and mathematical background with knowledge of supervised and un-supervised machine learning methods;

• Strong coding skills with a statistical scripting language such as R, Python and or SAS;

• Strong problem solving acumen with ability to breakdown complex problems, specifically feature engineering knowledge for model development

• Prior experience and knowledge of working within a Hadoop framework and big data technologies such as Sparklyr and HUE

• Highly motivated self-starter that takes initiative and has the ability to effectively organize, multi-task and prioritize a wide array of projects;

• Prior experience working in a global team environment preferred, but not required;

• Procurement, Risk and Operational knowledge preferred, but not required

Education:

BS degree in Computer Science, Mathematics, Statistics or Engineering required

MS degree in Mathematics or Statistics preferred

Skills

• Excellent communication skills with the ability to interface with technical and non-technical audience and work well in a team environment;

• Expert level technical knowledge using big data tools and techniques to breakdown complex data problems and create robust data models;

• Expert level business analysis skills to include problem solving, decision making, relationship building and management, detail oriented, documentation management;

• Expert level of financial analytics knowledge to support insight analysis with the information available;

• Ability to work with data from many different sources to build a logical data flow to support data modeling

• Analytical and statistical skills with the ability to recognize data patterns, work of data intuition and continuously ask “why”;

• Strategic problem solving and opportunity identification;

• Advanced communication/presentation skills as well as the ability to collect and share constructive feedback;

• Possess cross-cultural and cross-functional collaboration skills

• Strong work ethic, good attitude and team player

Competencies

• Conveys a sense of urgency and drives issues to closure, persists despite obstacles and opposition;

• Well organized when working under pressure;

• Ability to work and thrive in a flexible matrix organization with focus on networking;

• Navigates effectively in a changing environment where competing priorities require flexibility.

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Grade :All Job Level - All Job FunctionsAll Job Level - All Job Functions - US

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Time Type :Full time

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Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity.

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