Data Science Leadership Position in Fraud Detection
1 week ago
At Mastercard, we're committed to creating a sustainable economy where everyone can prosper. Our technology and innovation empower businesses, governments, and individuals worldwide.
We're looking for a skilled Data Scientist to lead our Security Solutions team in developing cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) models. These models will be integrated into our flagship product, enhancing authentication and authorization processes.
As a Lead Data Scientist, you'll work closely with business owners to understand requirements, performance metrics, and data quality. Your responsibilities will include:
- Collaborating with stakeholders to define business objectives and model performance indicators.
- Developing advanced fraud detection models for credit and debit card transactions.
- Implementing best practices to maintain the competitiveness of our fraud detection models.
- Overseeing the development and deployment of data and model pipelines.
- Investigating fraudulent patterns and trends to enhance model performance.
- Evaluating model robustness and readiness for deployment.
To succeed in this role, you'll need:
- Strong experience in data science and machine learning, particularly with XGBoost.
- Proficiency in SQL and familiarity with Linux/Bash environment, Python, and Pyspark.
- A bachelor's degree in Computer Science or a related field.
Nice-to-have qualifications include:
- Experience building payment fraud detection models.
- Graduate degree in Computer Science, Data Science, or a related field.
- Familiarity with data engineering on petabyte-scale data using PySpark.
Our Vancouver office offers a competitive salary range of $122,000 - $195,000 CAD per year. We're an equal opportunity employer, committed to diversity, equity, and inclusion.
In this role, you'll be responsible for maintaining information security and adhering to Mastercard's guidelines. You'll receive regular security trainings and must report any suspected security breaches.
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