Senior Data Scientist

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Aplazo

WebsiteLinkedInAll Job Openings

51 - 200 employees

💳 Fintech

🛍️ eCommerce

🤝 B2B

Fintech • eCommerce • B2B

Aplazo is a Mexico-based Buy Now, Pay Later (BNPL) platform that offers installment payment solutions and marketing tools to physical and online merchants, enabling consumers to purchase products through payments in installments. The company partners with brick-and-mortar and e-commerce merchants to provide point-of-sale financing and merchant-facing marketing tools, with a stated mission to unlock financial access, freedom, and opportunity for every Mexican.

📋 Description

• Contribute across Credit Risk (underwriting, fraud, collections), Growth (personalization, recommendations, incentivization), and AI initiatives based on team priorities • Own projects end-to-end within your assigned domain, data, modeling, deployment, and post-launch monitoring • Develop deep familiarity across multiple domains and systems, enabling the team to move faster and maintain continuity as we scale • Drive research and exploration workstreams, evaluating new modeling approaches, alternative data sources, and tooling that keeps Aplazo ahead of the curve • Collaborate closely with Lead and Staff Data Scientists to deliver on the broader roadmap • Identify and flag opportunities for improvement across domains, you'll often be the person with the broadest view of what's working and what isn't

🎯 Requirements

• 4–8 years of experience across more than one data science domain, Risk, Growth, Personalization, or similar • Strong ML fundamentals, you can pick the right tool for the problem rather than defaulting to what you know best • Demonstrated ability to ramp quickly on unfamiliar problems and deliver with limited guidance • Proficiency in Python, SQL, and standard ML frameworks • Hands-on experience with LLMs / GenAI as part of your regular workflow • Production experience, you've taken models past the notebook stage • Fintech or BNPL background, familiarity with credit risk or fraud modeling concepts (Strong Plus) • Experience with experimentation frameworks and A/B testing at scale (Strong Plus) • Exposure to MLOps practices, feature pipelines, model monitoring, deployment (Strong Plus) • Publications or presentations in recognized Machine Learning and Data Science journals / conferences (Strong Plus)

🏖️ Benefits

• Health insurance • Professional development opportunities