Senior Data Scientist
Develop and deploy machine learning models for real-time fraud detection and financial crime risk, while improving data quality, monitoring, and production reliability. Requires 5+ years working with large datasets, 3+ years of ML experience, and strong Python and SQL skills.
About the job
Responsibilities
- Build, validate, and deploy machine learning models to identify and prevent fraud in real time.
- Improve model reproducibility and robustness through documentation, testing, and monitoring.
- Ensure data quality and reliability across pipelines and tools.
- Collaborate with Risk Strategy to develop model inputs and applications.
- Partner with Engineering to optimize model deployment and observability.
Requirements
- 5+ years of experience working with and analyzing large datasets to solve problems and drive impact.
- 3+ years of machine learning experience.
- Proficiency in SQL and experience understanding and managing imperfect data.
- Proficiency in Python, statistical modeling, and machine learning.
- Experience deploying and monitoring machine learning models in production.
- Comfort working in a fast-paced environment with evolving priorities.
Nice-to-haves
- 1+ years of relevant risk experience.
- Familiarity with large language models or other generative AI and their applications to risk or fraud detection.
- Experience with modern data pipeline and ETL tools, such as dbt.
- Experience with model governance in finance or other regulated industries.
- Experience building zero-to-one solutions in ambiguous or greenfield problem spaces.
Compensation
- US employees: $166,600–$250,900 USD base salary, plus equity and benefits.
- Canadian employees: $157,400–$237,100 CAD base salary, plus equity and benefits.
Skills
Python, SQL, Machine Learning, Statistical Modeling, Fraud Detection, Model Deployment, Model Monitoring, Data Pipelines, ETL, dbt, LLMs, Generative AI, Model Governance
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