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Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (Fraud/Fintech): Building and productionizing machine learning models and rules-based systems that detect fraud, platform abuse, and identity theft with an accent on large-scale statistical modeling, data architecture, and fraud prevention. Focus on augmenting decisions with first- and third-party data, deploying models to production, and minimizing friction for legitimate users.
Location: Hybrid in New York, NY, or remote within the United States. Relocation expense coverage is available to NYC or San Francisco if needed.
Estimated base salary: $200,000–$330,000 per year, plus equity. Final compensation depends on location and hiring level.
Company
Ramp builds smart financial infrastructure that automates payments, spend management, risk detection, and accounting for businesses.
What you will do
- Build core machine learning models and rules-based systems to detect fraud, platform abuse, and identity theft.
- Apply statistical and machine learning techniques to large datasets to identify fraud patterns.
- Prototype, productionize, and analyze machine learning models in partnership with product and engineering teams.
- Collaborate with Fraud Engineering and Data Platform teams to combine first- and third-party data sources.
- Help shape scalable tools, processes, systems, and strategic roadmaps for fraud machine learning.
Requirements
- Bachelor’s degree or higher in mathematics, economics, physics, computer science, or another quantitative field.
- At least 5 years of industry experience as a Machine Learning Engineer, Applied Scientist, or Data Scientist.
- Strong Python experience with NumPy, pandas, scikit-learn, PyTorch, and other machine learning techniques.
- Experience deploying machine learning models to production and contributing to backend systems.
- Strong SQL knowledge, including Snowflake or PostgreSQL.
- Fluency with agentic AI tools for software development and data analysis.
Nice to have
- PhD in a quantitative field.
- Experience with fraud or identity threat detection systems.
- High-growth startup experience.
- Experience with the modern data stack, including Snowflake, Hex, dbt, or RisingWave.
- Experience developing LLM-backed systems or tools.
Culture & Benefits
- Flexible paid time off and centralized home-office equipment ordering.
- Medical, dental, and vision coverage, with additional regional healthcare options.
- 401(k) with employer match and other retirement plan benefits depending on location.
- Parental leave of up to 16 weeks for birthing and bonding or 8 weeks for bonding only, at 100% pay.
- Health and wellness stipend, fertility benefits, pet insurance, and employee assistance resources.
- In-office meals, snacks, drinks, coffee stipend, and budget for intra-office travel.
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