Selling Points of Position:
• Working with client’s innovation team
• Best Vs Average Candidate: Someone with experience in developing ML models as well as automated solutions.
• How will performance be measured: Verbal
• Tell us your story. Don’t go unnoticed. Explain why you’re a winning candidate. Think “client” if you crave meaningful work and embrace change like we do. We are a trusted North American leader that cares about people and inspires them to grow and move forward.
• Stay current and competitive. Carve out a career for yourself. Grow with client.
• The MD Innovation team is part of the Market Risk and Model Development (MRMD) within Risk Management at client. It is responsible for developing “best in class” machine learning (ML) models as well as automated solutions to tackle a wide range of emerging challenges in risk management. The focus of MD Innovation is to develop novel ML-oriented risk models that are used in a wide variety of applications to measure risk and adjudicate credit for all Canadian and US retail and non-retail lending portfolios, as well as quantify operational and market risk metrics.
• The group is hiring a 12-month Data Scientist contractor (Senior Analyst Level) to develop, enhance, and implement state-of-the-art machine learning (ML) models with a focus on credit risk modeling. Detailed responsibilities are as follows:
• You use various machine learning algorithms combined with big data techniques to create robust, high performance, and scalable models.
• You play a key role in project activities and deliverables that require collaboration and communication with a variety of stakeholders.
• You apply your expertise and ideas to address unique or ambiguous situations and to find solutions to problems that can be complex and non-routine.
• You provide technical leadership on the projects you work on and offer mentorship to more junior members of the team.
• Graduate degree in a quantitative field, such as Engineering, Computer Science, Physics, and Mathematics.
• Strong knowledge of machine learning and statistical learning algorithms, supervised learning, and unsupervised learning dealing with heavily imbalanced datasets.
• Have a deep understanding of tree-based techniques such as XGBoost and be familiar with the concepts of model explainability.
• Strong programming skills in Python and PySpark. Comfortable in version control tools such as Git and knowledge of cloud environments such as Azure ML.
• Experienced in working in Big Data Analytical environments/technologies (Hadoop, HIVE, Spark), with a deep understanding of data mining and analytical techniques.
• Experienced in working with relational databases.
• Strong knowledge of economics, financial services, and banking products.
• Out-of-the-box thinker who seek out innovative solutions and embrace evolving technologies.
• Education in Engineering, Computer Science, Physics, and Mathematics. Is required
• Knowledge with Python and PySpark is required
• 2+ years experience working in Big Data Analytical environments/technologies (Hadoop, HIVE, Spark),
Nice to Have:
• Working knowledge of capital and/or retail credit risk is preferred.