Data Scientist II
- Toronto, ON
- Hybride
- Publié 18 sept. 2026
- 1 poste
Ouvre un site externe
- Type d’emploi
- Contrat
- Niveau d’expérience
- Intermédiaire · 2+ ans
- Formation minimale
- Baccalauréat
- Postuler avant le
- 14 oct. 2026
- Langue de l’offre
- anglais
- Heures de travail
- 40 heures par semaine
- Présence au bureau
- 2 jours par semaine
- Niveau d’expérience
- Entry level
Résumé du poste
The role involves evaluating third-party machine learning models for customer identity verification and translating technical documentation into internal standards. You will analyze model performance and present findings to stakeholders within a fraud prevention and model governance framework.
Détails du poste
Toronto or Markham hybrid (two days onsite), this opportunity is with a leading financial institution. The team is seeking a Data Scientist, specifically a Business Information Management Analyst, for an initial six-month engagement with the possibility of extension. The role focuses on Python, SQL, machine learning methodology, model performance analysis, model governance, Databricks and PySpark. The main feature of this opportunity is the chance to work at the intersection of data science, fraud prevention and enterprise model governance without being limited to routine data manipulation or model building. You will evaluate third-party machine learning models that help verify customer identity, translate complex technical documentation into clear internal standards, and present your findings to stakeholders. This is an excellent opportunity for a quantitatively strong analyst who enjoys understanding how models work, challenges their performance and communicates technical concepts clearly. Contract Duration: 6 months (extension possible) Required Skills & Experience Bachelors or Masters degree in Data Science, Statistics, Mathematics, Computer Science or a related field 2+ years of experience in analytics, data science, model governance, model risk, fraud analytics or financial crime analytics Strong Python and SQL skills Practical understanding of machine learning methodology Experience with classification, regression, ensemble methods and anomaly detection Desired Skills & Experience Experience with Databricks and PySpark Exposure to model validation, model risk management, compliance or audit Banking, financial services or other regulated-industry experience Familiarity with customer identity verification, fraud detection or access management models What You Will Be Doing Tech Breakdown 40% Python, SQL, Databricks, and PySpark 35% Machine learning 25% Model documentation, governance and third party vendor model review Daily Responsibilities 35% Hands On Duties 25% Management Duties 40% Team collaboration The Offer You Will Receive The Following Benefits Statutory Vacation Holiday Pay Posted By: Emma Borges
Ce que vous ferez
The role involves evaluating third-party machine learning models for customer identity verification and translating technical documentation into internal standards. You will analyze model performance and present findings to stakeholders within a fraud prevention and model governance framework.
Exigences
Candidates need a degree in Data Science, Statistics, or a related field with over 2 years of experience in analytics, model governance, or fraud analytics. Proficiency in Python, SQL, and machine learning methodologies is required.
Avantages
• Statutory Vacation • Holiday Pay
Compétences indiquées
- Technical Documentation · Souhaitée
- SQL · Souhaitée
- Apprentissage automatique · Souhaitée
- Python · Souhaitée
Autres compétences pertinentes
Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.
- Python
- Sql
- Machine Learning
- Model Governance
- Databricks
- Pyspark
- Model Performance Analysis
- Fraud Prevention
- Classification
- Regression
- Ensemble Methods
- Anomaly Detection
- Model Risk Management
- Customer Identity Verification
- Technical Documentation
- Stakeholder Presentation
Domaines d’emploi
- Data & Analytics
- Finance & Accounting
- Technology
- Science & Research
- Engineering
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