1 день назад
Staff Data Scientist (Machine Learning)
239 000 - 298 800$
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Data Scientist (Machine Learning): Building, validating, and deploying real-time machine learning models that detect and prevent fraud, while improving data quality and model reliability with an accent on risk analytics, production deployment, and model monitoring. Focus on designing robust fraud defenses, prototyping risk solutions, and aligning product, engineering, and risk teams around scalable machine learning systems.
Location: San Francisco, New York, Portland, or remote within Canada or the United States.
Salary: US employees: $239,000–$298,800 USD; Canadian employees: $225,900–$282,400 CAD. Total rewards also include equity and benefits.
Company
Mercury is a fintech and SaaS company providing banking services through partner financial institutions.
What you will do
- Build, validate, and deploy machine learning models to identify and prevent fraud in real time.
- Document, test, and monitor models to support reproducibility, robustness, and production reliability.
- Ensure data quality and reliability across data pipelines and analytical tools.
- Collaborate with Risk Strategy to develop model inputs and applications.
- Partner with Engineering to optimize model deployment and observability.
- Act as a technical lead by prototyping solutions, codifying best practices, and empowering other team members.
Requirements
- 7+ years of experience working with and analyzing large datasets, including 5+ years of machine learning experience.
- Proficiency in SQL and experience managing imperfect data.
- Proficiency in Python, statistical modeling, and machine learning.
- Experience deploying and monitoring machine learning models in production.
- Ability to lead, empower others, and drive alignment across teams with different roadmaps, timelines, or architectures.
- Ability to work effectively in a fast-paced environment with evolving priorities.
Nice to have
- 1+ years of relevant risk experience.
- Familiarity with LLMs or other generative AI applications for 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 environments.
Culture & Benefits
- Collaboration across product, engineering, and risk teams.
- Work focused on fraud prevention, customer protection, and financial crime risk.
- Base salary, equity through stock options or RSUs, and benefits.
- Commitment to diversity, belonging, equal employment opportunity, and reasonable recruitment accommodations.
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