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Principal Data Scientist

Principal Data Scientist

Farm Credit CanadaToronto, ON, Canada
9 days ago
Salary
CA$117,887.00–CA$159,495.00 yearly
Job type
  • Permanent
Job description

Closing Date (MM / DD / YYYY) : 02 / 14 / 2025

Worker Type : Permanent

Language(s) Required : English

Term Duration (in months) :

Salary Range (plus eligible to receive a performance based incentive, applicable to position) : $117,887 - $159,495

Experienced data scientist needed :

Collaborate with cross-functional teams to define data requirements and ensure data quality. As a Principal Data Scientist, you’ll lead the design and development of digital twin frameworks, ensuring seamless integration with existing systems. Plus, you’ll lead the development and implementation of data science projects that focus on large datasets, applying advanced statistical and machine learning techniques to extract insights from complex data.

What you’ll do :

  • Lead and mentor data teams to find problems, and to solve and capitalize on opportunities within huge and diverse datasets.
  • Unlock insights from the most complex and diverse groupings of internal and external data, and tell a story with the data that enables enlightened decision making at all levels of the organization.
  • Use advanced big data, predictive analytics, and machine learning tools to solve complex data problems and improve FCC through data.

What we’re looking for :

  • Enterprise-wide thinker who can combine rare and deep business domain knowledge with complex statistical and mathematical concepts.
  • Confident communicator able to translate knowledge for others.
  • Expert in statistics and mathematics.
  • Relationship-builder comfortable making recommendations to senior leaders.
  • Proven mentor who works well within a high-performing team.

    • What you’ll need :

  • A master’s degree in one of the noted domains and at least eight years of experience (or an equivalent combination of education and experience).
  • Expert knowledge of risk management and finance areas such as credit risk, allowance for credit losses, economic capital, and stress testing.
  • Demonstrated ability to work with huge data sets and use sophisticated big data and machine learning techniques and technologies.
  • Mastery of one or more predictive and descriptive analytics tools and techniques.

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