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Data Scientist (Quantitative Analytics Specialist 3)

Job Description:
Important Note: During the application process, ensure your contact information (email and phone number) is up to date and upload your current resume when submitting your application for consideration. To participate in some selection activities you will need to respond to an invitation. The invitation can be sent by both email and text message. In order to receive text message invitations, your profile must include a mobile phone number designated as "Personal Cell" or "Cellular" in the contact information of your application. At Wells Fargo, we want to satisfy our customers' financial needs and help them succeed financially. We're looking for talented people who will put our customers at the center of everything we do. Join our diverse and inclusive team where you'll feel valued and inspired to contribute your unique skills and experience. Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you. Data Management and Insights (DMI) is transforming the way that Wells Fargo uses and manages data. Our work enables Wells Fargo to empower and inform our team members, deliver exceptional experiences for our customers, and meet the elevated expectations of our regulators. The team is responsible for designing the future data environment, defining data governance and oversight, and partnering with technology to operate the data infrastructure for the company. This team also provides next generation analytic insights to drive business strategies and help meet our commitment to satisfy our customers' financial needs. This role is a part of DMI's Enterprise Analytics and Data Science Team - the central analytics group tasked with solving high-impact business challenges for the Enterprise and standing up cutting-edge analytical capabilities to be shared across Wells Fargo's analytic community. We are looking for a data scientist to join our team and help us solve challenging and interesting business problems through rigorous data analysis, predictive modeling, and design of complex analytic systems. In this highly technical role, you will support Enterprise Personalization Program. This initiative is focused on using ML/AI algorithms to develop personalized customer experience and marketing programs. As part of the core Personalization Data Science team, you will collaborate with other data scientists to generate innovative ideas, create hypotheses, design quantitative analyses and experiments, build predictive models using ML techniques, and generate business insight. KEY RESPONSIBILITIES INCLUDE: Conduct exploratory data analysis, mine data (e.g., clustering), and prepare modeling datasets from multiple data sources. Build, validate, and implement predictive models using machine learning algorithms (e.g., Random Forests, GBM, neural networks, SVM, Na ve Bayes Classifier), as well as traditional statistical modeling techniques (e.g., time series forecasting, linear and logistic regression). Conduct statistical analyses, design in-market experiments, respond to ad-hoc requests from business partners to identify/quantify opportunities or address specific questions. Present model results and analytic findings, lead discussions, provide insight and actionable recommendations to business partners to support data driven and evidence based decision-making. Work with complex databases, conduct research to identify data issues, propose solutions to improve data integrity; perform other database-related analyses and projects as requested; collaborate with data engineers to help optimize data retrieval processes to support ML algorithm automation. Utilize emerging analytic and programming techniques to explore internal and external unstructured and semi-structured data; recommend how these additional data sources can be used to enhance existing models and provide additional insight. Required Qualifications 2+ years of experience in an advanced scientific or mathematical field A master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science 3+ years of SQL experience 3 + years of experience using quantitative machine learning techniques 3+ years of Python experience Desired Qualifications 1+ year of Big Data experience Experience with Spark, Hive and Kafka 3+ years of statistical modeling experience Other Desired Qualifications Advanced degree in quantitative discipline (e.g., Statistics, Economics, Computer Science, Applied Mathematics). Strong programming skills using advanced tools like Python, SAS, PySpark, H2O, Hive and/or SQL with ability to write efficient code to manipulate data for analytical purposes, conduct statistical analysis, and develop predictive models. Ability to learn new technologies quickly. Advanced knowledge of statistical methods (e.g., probability, multivariate data analysis, regression, PCA, time-series analysis) and substantial hands-on experience with machi

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