Data Science Lead

Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds,  and make a meaningful impact, we want to hear from you.

Do you enjoy solving problems, looking at problems through a different lens, and working closely with customers to develop new innovative solutions? Are you interested in utilizing your skills in machine learning to catch fraudsters?

Equifax is looking for a Data Science Lead / Principal Data Scientist to join our Equifax Digital Solutions (EDS) team. This opportunity will need to be located in either our Boise, ID or Alpharetta, GA office and will need to adhere to our hybrid work schedule (Monday - Thursday in office).

What is Equifax Digital Solutions? 

Equifax Digital Solutions (EDS) has developed a powerful suite of solutions designed to help global businesses navigate the complexities of identity resolution and fraud prevention in the digital age. EDS currently provides digital identity and fraud prevention solutions to thousands of global merchants, financial institutions,  and payment providers.  At its core, the EDS Data & Analytics team leverages Equifax's vast data assets and advanced machine learning to provide solutions for verifying and authenticating identities, assessing risk, and protecting consumers against fraudulent transactions.

In EDS, we're committed to collaborative innovation, working hand-in-hand with our customers to develop solutions that tackle their most pressing identity resolution and fraud prevention challenges.

What you’ll do

We're seeking a Data Science Lead / Principal Data Scientist, with a proven track record in machine learning and AI,  to lead our payments and transaction protection solutions. The ideal candidates will possess experience in the financial services industry and/or consulting and have a passion for applying machine learning to real-time business problems. As a key member of our team, you'll collaborate closely with our engineering teams and strategic customers to develop cutting-edge payment and transaction monitoring solutions.  

Subject matter expertise 

• Lead data-science machine learning and AI projects focused on real-time payments fraud prevention. 
• Leverage your experience in machine learning and AI to drive data-science solutions for customers. 
• Collaborate with business partners, customers and stakeholders to define solution approach 
• Serve as our data science thought leader in payments and transaction fraud prevention. 

Hands on Data Science 

• Apply ML/AI frameworks and best practices for model building. 
• Deep mathematical understanding of statistics and machine learning 
• Incorporate best practices for machine learning(ML) and artificial intelligence (AI) building. 
• Review, support and provide best practices for analysis of machine learning(ML) and artificial intelligence (AI) models. 
• Evaluate ML model performance, explainability, and  selected modeling techniques are appropriate and align with desired project outcomes. 

Partner with the Business

• Lead ML/AI and data-science partnerships and engagements with customers.   
• Identify opportunities to improve existing models and exploit our intellectual property. 
• Partner with diverse Equifax teams to identify and explore opportunities for the novel application of machine learning (ML) and artificial intelligence(AI). 
• Provide insights and solutions by understanding the business, product, data, and customer perspective. 
• Develops new techniques/methodologies for building solutions for a specific business challenges based on available data. 

What experience you need 

• Masters Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, Engineering or related field AND 5+ years of hands on data-science experience (e.g., training and evaluating machine learning models, applying statistical techniques, and reporting results)
• Proven track record of designing and developing predictive models in real-world applications with a thorough understanding of diverse machine learning algorithms, including:
• Supervised Learning: Regression (linear, polynomial, logistic, etc.), classification  random forest, XGBoost, LightGBM, Catboost, SVMs, etc.),