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Sr. Analyst, Model Analytics and Validation (18 Month Contract)

Home / Sr. Analyst, Model Analytics and Validation (18 Month Contract)

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City : Toronto, Ontario, Canada

Category : Full time

Industry : Public Services/Utilities

Employer : Healthcare of Ontario Pension Plan (HOOPP)

Why you’ll love working here:

  • high-performance, people-focused culture

  • our commitment that equity, diversity and inclusion are fundamental to our work environment and business success, which helps employees feel valued and empowered to be their authentic selves

  • learning and development initiatives, including workshops, Speaker Series events and access to LinkedIn Learning, that support employees’ career growth

  • competitive, 100% company-paid extended health and dental benefits for permanent employees with recent additions to promote inclusive coverage to a diverse employee population. These recent additions include gender affirmation and fertility drug and treatment coverage

  • membership in HOOPP’s world class defined benefit pension plan, which can serve as an important part of your retirement security

  • access to an annual wellness reimbursement program for health and wellness-related expenses for permanent employees

  • virtual fitness, yoga and meditation classes, nutritional consultations and wellness seminars

  • we offer a hybrid flexible work model that embraces remote work in Ontario for eligible roles

  • the opportunity to make a difference and help take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers

Job Summary:

Under the guidance of the Senior Director, Model Analytics and Validation, the Sr. Analyst, Model Analytics and Validation will be accountable for ensuring the integrity of the risk system and the reports that it produces. The incumbent will help to develop new processes and reports for measuring and monitoring risk using advanced quantitative techniques and software tools.

What you will do:

  • Model derivatives, hedge funds, liabilities, and other securities within the risk management system. Review the models on a regular basis and ensure that the risk embedded is well measured and monitored. Work with traders, finance, and developers to add new instruments and models as required

  • Design and implement risk reports, dashboard, and other visualization tools to provide insights into the risk of HOOPP for target audiences in the organization, including senior executive committees and the Board of Trustees

  • Leverage strong analytical capabilities in investment risk analysis and excellent writing and communication skills to ensure analysis and comments are communicated effectively through risk reports

  • As an expert in the risk management system, incumbent will also work closely with other groups at HOOPP to help them utilize the system to its full capacity as well as assist the Senior Director with risk related questions

  • Foster continual innovation and thought leadership by leveraging state-of-the-art industry intelligence and in-house risk expertise across the Risk Division

  • Use statistical methods and machine learning to incorporate instruments into the risk management system

  • Validate models used in the risk management system

What you bring:

  • Minimum of 4-7 years of professional experience in the financial industry including experience in a risk management department

  • Graduate degree in Finance, Math, Statistics, Business Administration, Economics, or related field

  • Excellent writing, editing and problem-solving skills

  • Possess strong technical skills. Some programming knowledge is required, Python is preferred.

  • Possess strong quantitative skills. Knowledge of machine learning / big data is an asset

  • Knowledge of risk management, derivatives and quantitative/statistical techniques

  • Strong interpersonal skills with solid verbal and written communication skills

  • Demonstrated ability to collaborate and work with a team, while also being self-motivated and able to work independently

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