Data Scientist - Fraud Specialist

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 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 be responsible for leveraging your expertise in data science and machine learning to develop, implement, and enhance cutting-edge fraud detection and prevention systems. This is an exciting opportunity to make a tangible impact on security and risk management within a dynamic financial environment.

What you will do:

• For the first 3-6 months you will learn our data, our technologies, our platform and work on exciting projects to support our advanced analytics team.

• You will design, develop, and implement advanced machine learning models specifically for fraud detection, prevention, and investigation across various financial products and services (e.g. credit applications)

• You will conduct thorough analysis of large datasets to identify anomalies, patterns, and indicators of fraudulent activity

• You will utilize a variety of statistical and machine learning techniques relevant to fraud detection, including classification algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting), anomaly detection methods (e.g., isolation forest, one-class SVM), and network analysis

• You will work with key clients on custom projects and solutions through the development of custom scores, strategy optimisation, benchmarking and reporting reports and co-innovation projects.

• You will effectively communicate analytical results to key stakeholders using strong data visualizations, superior presentation skills and business language to emphasize the “so what” of any analysis performed.

• You will stay current with the latest trends and techniques in fraud detection, machine learning, and the evolving landscape of financial crime.

What experience you will need:

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

• 3+ 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).

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

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

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

What could set you apart (nice to have skills):

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

• Experience working with credit data 

• 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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