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10 дней назад

Data Scientist, Model Performance Monitoring and Reporting

Формат работы
hybrid
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
Canada
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Data Scientist, Model Performance Monitoring and Reporting (Fraud Detection/AI): Monitoring and reporting on the performance of existing fraud detection models using SQL, Python, and statistical analysis with an accent on model oversight, reusable monitoring processes, and regulatory compliance. Focus on investigating performance changes, building automated monitoring pipelines, transitioning SAS processes to Databricks, and communicating findings to US Fraud business partners.

Location: Toronto, ON; hybrid arrangement with 1–3 days per week on-site and remaining days remote. Candidates must be legally eligible to work at the specified location and may need a valid work or study permit.

Company

hirify.global is a relationship-oriented bank whose Advanced Analytics and AI team applies data science, machine learning, artificial intelligence, predictive science, and big data technologies.

What you will do

  • Monitor and report on the performance of internal and external fraud detection models using statistical methods.
  • Investigate changes in model results, identify performance concerns, and prepare accurate, timely reporting for business oversight.
  • Use SQL and Python to query, prepare, and analyze data, calculate performance measures, and develop reusable code.
  • Contribute to automated monitoring pipelines and the transition from existing SAS processes to Databricks.
  • Interpret monitoring results within the model development, evaluation, deployment, and monitoring lifecycle.
  • Partner with US Fraud stakeholders, data scientists, and technical leads to explain findings, document processes, and improve monitoring practices.

Requirements

  • Experience with SQL and Python, including querying, combining, preparing, and analyzing data with an emphasis on accuracy, readability, and repeatability.
  • Strong foundation in statistics, including applying statistical concepts to evaluate model performance, interpret results, and investigate changes over time.
  • Understanding of the machine learning model lifecycle, including development, evaluation, deployment, and monitoring.
  • Degree or diploma in statistics, mathematics, computer science, engineering, or a related quantitative discipline, or equivalent practical experience.
  • Ability to investigate complex problems, interpret information, communicate detailed findings meaningfully, and explain technical concepts to different audiences.
  • Ability to ask thoughtful questions and build effective relationships with business and technical partners.
  • Curiosity, initiative, willingness to learn, and commitment to high standards and continuous improvement.

Culture & Benefits

  • Hybrid work environment with flexibility to manage work activities.
  • Competitive salary, incentive pay, banking benefits, and a benefits program.
  • Defined benefit pension plan, employee share purchase plan, vacation offering, and wellbeing support.
  • Social recognition program and a paid Purpose Day for personal growth and development.
  • Inclusive and accessible candidate and workplace experience.

Hiring process

  • May include an attribute-based assessment and skills tests such as simulation, coding, or French proficiency assessment.
  • Artificial intelligence tools may be used during the recruitment process.

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