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 the Lead 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 deliver state of the art decision science models & 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, support redesigning data modeling processes, create new algorithms and predictive models the business needs, and lead analysis of the data and sharing insights with peers. 

If you have never managed or built a team before, this is a great opportunity to gain that experience coaching some of the brightest Data Scientists in Canada earlier in their career, offering them mentorship and advice to improve their effectiveness.

What you will do:

• Help lead the vision and strategy of Data Science for Equifax Canada (a Centre of Excellence).

• Develop new tools, advanced analytical techniques and products.

• Work with key clients, stakeholders to support development of proprietary analytical products, custom scores as part of co-innovation projects and effectively communicate analytical results to key stakeholders using strong data visualizations, superior presentation skills and business language to emphasize the “so what” of the analysis.

• Support redesign of the data science model development lifecycle using the latest techniques and methodologies and inventing new ones where needed.

• Develop new attributes which add value to business decisions

• Project management including defining business and technical requirements, resource planning and analytical solution design.

• Provide recommendations and market insights that support solving complex business problems

• Ensure quality control of all analytical output by junior and intermediate data scientists.

• Coach and mentor junior and intermediate data scientists in career development and data science skill-set improvement.

What experience you will need:

You don’t have to tick all of the bullets below, but some of the following would be essential:

• 5+ years’ data science experience with expert knowledge of Python, SQL, R or SAS in a large data environment.

• 3+ years experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks (including LSTMs, RNNs).

• 5+ years’ proven hands-on experience designing, building and implementing analytical solutions to solve real world problems.

• 2+ years’ experience building models with the best packages including scikit learn, XGBoost, Tensorflow, PyTorch, Transformers.  

• 2+ years’ experience developing and leading the technical vision of an organization and working independently and closely with senior leadership to lead data science to continued success into the future.

• 1+ years’ background in and an innate talent and passion for trying new technologies and quickly assessing value and implementability within organizations.

• Bachelor’s or advanced degree in a quantitative discipline such as Engineering, Economics, Mathematics, Statistics, or Physics is essential.

What could set you apart:

• A background in financial services, credit, telecommunications or utilities.

• Experience working with credit or fraud data and Experience in leadership and mentorship

• Experience with development and deployment of models in a cloud based environment such as AWS or GCP is preferred.

• Master’s level degree in a business-related field/MBA.

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