Senior Machine Learning Engineer, Credit Risk
hace 1 mes
We're a Mexico City-based fintech startup revolutionizing unsecured credit for underserved communities. Our innovative approach combines physical onboarding kiosks with a documentless 3-minute video onboarding process, bridging the gap for those in remote communities. This asset-light strategy, paired with a superior risk model based on computer vision and natural language processing, enables us to offer the lowest interest rates available to today's underserved.
Key ResponsibilitiesWe're seeking a seasoned Machine Learning Engineer to drive innovative approaches to credit decisioning: approving new clients, offering new loans to past clients, determining loan parameters, and deciding collection actions.
Our team is passionate about tackling complex business problems, and we welcome fresh ideas to enhance our risk model. In the medium-term, we envision our ML Engineer leading new projects end-to-end, from stakeholder conversations to deployment and monitoring of models.
RequirementsEssential Skills- STEM degree
- 3+ years of professional experience in machine learning on tabular data
- Strong Python skills
- Familiarity with Git
- Creative problem-solving and strategic thinking
We're open to candidates with a mix of the following skills:
- Master's degree
- Track record of designing high-performing ML models for tabular data
- Financial industry experience
- Experience with auto-regressive models (e.g., Recurrent Neural Networks)
- Reinforcement Learning expertise
- Advanced Python skills (deep knowledge, clean code, and scalable architecture)
- End-to-end ML system experience, cloud platforms, data pipelines, and model monitoring
- Familiarity with our tech stack: LightGBM, DVC, Pants, FastAPI, Docker, GitHub Actions, TrueFoundry
- Competitive compensation package with stock options
- Fast-paced environment with growth opportunities
- Relocation package
- 15 annual vacation days + 7 annual personal days
- Flexible remote work options (3-4 days/week) or fully remote work arrangements
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