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Data Scientist Specialist

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Equifax is looking for a Data Scientist Specialist to join our world-class Data and Analytics team. Data and Analytics, the advanced analytical division of Equifax, offers state of the art solutions to current and emerging business challenges. Through our data analytics, customer insights, modeling, and consulting services, we deliver the analytics and insights companies need to enhance their business. In this exciting role, you will have the opportunity to work on a variety of challenging statistical and analytical projects to develop product prototypes, testing new modeling techniques, insights on existing Equifax assets, M&A due diligence, assessment of new data assets, and product enablement. You will have the opportunity to work using cutting edge cloud technology and advanced statistical techniques.

This role requires understanding large, diverse data sources, extensive analytics experience and strong technical skill. This person will be considered as the analytical expert in the team. This role includes participating in discussions with internal customers during discovery and design phases, performing the statistical analysis, and presenting the findings to internal audiences. 

What you’ll do

  • Work with Business Leads, Product Management, other Statisticians, and Data Analysts on the formulation and application of new statistical insights related to the improvement of our data assets

  • Execute on projects to develop product prototypes

  • Manipulate large data sets, integrate diverse data sources, data types and data structures into solutions

  • Research new and advanced predictive modeling techniques as appropriate for a specific product prototype

  • Explore new techniques and technologies related to data and statistical analysis and testing on potential new products

  • Execute complex projects combining different data assets

  • Expertly profile data and develop in depth analytics on the value of new data

  • Understand internal customer business needs and translate them into data-driven insights

  • Contribute to process improvement initiatives within entire Data and Analytics group

What experience you need 

  • Master’s degree or higher in Data/Computer Science, Statistics, Mathematics, Economics or other related discipline

  • 7 to 10 years of statistical and/or data analysis experience in the credit risk, telecommunications, financial services, marketing, fraud, or insurance analytics arena

  • 3 to 5 years of experience with Python, or a similar data scientist tools

  • ​3 to 5 years of experience with SQL

  • 5 plus years of strong quantitative analytical experience, including hands-on and background in statistics, various modeling techniques, and machine learning algorithms

What could set you apart

  • Strong knowledge of credit bureau data and other data assets including alternative data

  • Strong ability to develop data-driven solutions to improve data assets

  • Strong consultative acumen and ability to understand complex analytical solutions

  • Experience working in GCP environment preferred

  • Experience with state of the art machine learning algorithms such as neural networks, support vector machines, ensemble models, random forest etc

  • Extensive experience manipulating and processing a variety of big data assets

  • Ability to create new ideas for analytical solutions

  • ​Ability to work independently with limited supervision

  • Understanding of Neural Networks, Random Forest, Machine Learning and other innovative techniques

We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.

If this sounds like somewhere you want to work, don’t delay, apply today - we’re looking for you!

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

El trabajo en Equifax

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