Quantitative Analytics Specialist (Ref 000846)

Wells Fargo & Company•San Francisco, CA

18h•$120,000 - $196,000•Hybrid

About The Position

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. Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you. Wells Fargo Technology sets IT strategy; enhances the design, development, and operations of our systems; optimizes the Wells Fargo infrastructure footprint; provides information security; and enables continuous banking access through in-store, online, ATM, and other channels to Wells Fargo’s more than 70 million global customers. Wells Fargo Bank N.A. seeks a Quantitative Analytics Specialist in San Francisco, CA. Job Role and Responsibility: Execute on data science projects, follow the analytic plan set by the technical lead, actively participating in the development, deployment, monitoring of the model. Learn the practice of data science and data engineering within the organization and help its furthering through bringing in relevant research ideas. Work with our internal data science teams throughout Enterprise Analytics & Data Science, and possibly with other analytics teams across the enterprise, to provide data transformation and data wrangling functions using cutting edge advanced analytics tools and methods. Help define the monitoring and maintenance plan for the model. Play a role in the following areas: development of the data pipeline leading into model training and scoring and development of the model operationalization pipeline. Telecommuting is permitted up to 2 days a week. Position must appear in person to the location listed as the work address. Travel required: 0%

Requirements

Position requires a Master's degree in Statistics, Mathematics, Physics, Engineering, Computer Science, Economics, or related quantitative field.
Python or R experience with Machine Learning tools
Data transformation and data wrangling experience, using tools such as SQL
Exposure to quantitative machine learning techniques
Development experience with languages like Python, Java, Scala, or R
Solid understanding of machine learning techniques such as neural networks, random forest, GBM and SVM
Exposure to big data tools like Spark, Hive, Kafka, and Map Reduce
Experience with machine learning libraries such as MLlib, scikit-learn, H2O

Responsibilities

Execute on data science projects, follow the analytic plan set by the technical lead, actively participating in the development, deployment, monitoring of the model.
Learn the practice of data science and data engineering within the organization and help its furthering through bringing in relevant research ideas.
Work with our internal data science teams throughout Enterprise Analytics & Data Science, and possibly with other analytics teams across the enterprise, to provide data transformation and data wrangling functions using cutting edge advanced analytics tools and methods.
Help define the monitoring and maintenance plan for the model.
Play a role in the following areas: development of the data pipeline leading into model training and scoring and development of the model operationalization pipeline.