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Equifax, Inc. Financial Services Data Wrangling Analyst - EQUIGLOBAL13778 in St. Louis, Missouri

Equifax Workforce Solutions has an exciting career opportunity for a Financial Services Data Wrangling Analyst within the Data and Analytics organization. Qualified candidates will have a strong academic background in mathematics, statistics, engineering or relevant field within the financial services industry. The candidate must have excellent big-data analytical skills and a business development mindset in order to drive customer co-innovation and construct best in class predictive models and analytical solutions for Equifax clients.Who is Equifax?Equifax is a global data, analytics, and technology company. We believe knowledge drives progress. We blend unique data, analytics and technology with a passion for serving customers globally, to create insights to power the decisions that move people forward. We serve as a consumer advocate, steward of financial literacy, and champion of economic advancement.As an innovative global company that enables access to credit, were part of breakthrough collaborations and innovations that address complex social challenges such as social welfare, community relations and financial education for underprivileged youth. We establish relationships that create economically healthy communities. We help individuals gain financial independence by increasing access to capital for small businesses. And we provide young adults entering college or university with financial education tools.Regardless of location or role, the individual and collective work of our people makes a difference in our business. We are Powering the World with Knowledge and looking for talented team players to join us as we help people live their financial best.The Perks of being an Equifax Employee?We offer excellent compensation packages with high-reaching market salaries, 401k matching, along with the works: comprehensive healthcare packages, schedule flexibility, collaborative work spaces, work from home opportunities, paid time off, and organizational growth potentialGrow at your own pace through online courses at Learning @ EquifaxWhat Youll DoAnalyze large dataset to extract actionable insights for internal and external audiences and leverage data visualization best practices to drive engagementDrive best in class custom analytics through predictive modeling, validation and analysis of customer data.Take ownership of delivering quality projects to our customers, applying rigor to project planning and design.Drive the DandA innovation pipeline by identifying new applications of our unique data assets and generating Proof of Concept analysis demonstrating the ROI.Work effectively and cooperatively with customers, co-workers, vendors, and management to create new ideas for analytical solutions that solve customer business problems.Work with Product Managers, Sales Consultants and Technology teams to drive new product ideas and ensure new solutions are implemented in an efficient manner.Qualifications1 years of experience in big data modeling and analytics, ideally focused on consumer risk strategy within financial services industryExcellent communication skills and customer-facing project management experience.Strong understanding of the financial services industry, regulations, and the data applied in marketing, risk, acquisitions, account management, and debt management lifecycle.Advanced degree in Operations Research, Industrial Engineering, Statistics, Mathematics, Economics or other related disciplines.Ability to clearly present statistical solutions to a non-technical audienceExcellent problem solving skills with the ability to design algorithms, which may include data cleaning, data mining, data clustering and subsequent data visualization and analytics.Expertise in data management, analytics, modeling, and statistics with hands on experience in Python, Hadoop, SAS or related statistical packages.Solid understanding of model development theory including machine learning techniques, and a working knowledge of Nave Bay

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