Data Scientist - Feature Engineering

Company: Equifax

Location: Toronto, Ontario

Posted: 2026-01-31

About this role

Synopsis of the role
Do you have a passion for being at the forefront of Data Science innovation and building cutting edge, scalable analytical solutions? Are you a tech savvy individual looking for an exciting, dynamic role to grow your career fast with one of the largest global data analytics and technology companies? Do you want to create new products and push business forward enabling Canadians to live their financial best? If you are a leader in designing & developing Machine Learning solutions blending science, art & business logic and unlocking the power of data to solve complex business problems, we would love to hear from you!


As a Data scientist within the Data & Analytics team at Equifax Canada, you will be critical to driving Data Science innovation, working closely with the rest of the Canadian Equifax Data Science & Insights team and the Data Science community internationally. You will partner with peers, internal stakeholders and external clients to design and deliver state of the art features / attributes that leverage Equifax’s vast data assets. These include decision areas covering the credit lifecycle, geodemographic & marketing attributes, ratings & fraud models, as well as any new areas where data driven decision making can be informed by predictive modeling including advanced modeling techniques and machine learning. You will extract the data you need, analyse the predictive power and potential business value of varying data types and design proprietary features leveraging these learnings.


What you will do:

  • For the first 3-6 months you will learn our data, our technologies and our platforms and support our advanced analytics team in your journey to becoming an Equifax data expert.
  • You will develop new tools, advanced analytical techniques and products.
  • You will develop new attributes which add value to business decisions including exploratory analysis across new alternative data sources, new data partnerships and expa...

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