Principal Research Scientist [Risk]
关于 Plata Plata 是世界上增长最快的金融科技公司之一。仅在3年内，我们已发展到3M+客户，达到5亿美元以上的估值。我们现在正在加强我们的风险和决策核心团队，并寻找一名主要人工智能工程师来设定技术标准，建立一流的生产级模型，从而显著地移动业务指标。为什么这个角色 · 你将拥有我们的基础模式计划 - 从研究方向到运行在生产和实际信用决定中的模型。·你的模型直接移动重要数字：投资成本风险，审批率和 NPV 跨越一个快速扩展的信贷组合。· 你将与 LATAM 中最丰富的金融数据集之一合作，每天都在增加3万+客户中的数十亿笔交易和行为事件。等待你的挑战 · 在金融事件序列 - 交易，信用局数据以及应用程序内行为的基础模型建立，并对下游业务任务进行细节调整：承载 (PD),信贷限制策略，欺诈检测，收藏和倾向模式。· 从端到端驱动技术决策：方法 →实施 →性能和延迟 →强度，解释性和监管合规。·从研究到生产：· 研讨行业最先进的方法，发表自己的作品，并在领先会议上讲话。· 导师高级工程师和科学家；在团队中制定模型 （设计审查，评估方法，部署实践）的技术标准。· 对产品，风险，业务和领导力等跨职能利益相关者明确沟通结果：什么让你适合？· 经验证明应用深度学习到序列数据 - 转型结构对事件/交易序列强烈偏好但不要求。· 强大的基础在数学统计和概率理论。· 深入了解机器学习算法 （GBM,MLP,CNN,RNN,变压器等）·经验将大型模型投入生产：分布式培训，模特服务，延迟/成本权衡。·能够达到解决方案复杂性与实用性之间的合理平衡。· 强的数学或技术教育 - 数学，物理或 CS 学位从一到二年
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About Plata
Plata is one of the fastest-growing fintech companies in the world. In just 3 years, we've grown to 3M+ customers and reached a $5B+ valuation. We're now strengthening our Risk & Decisioning core team and are looking for a Principal AI Engineer to set the technical bar and build best-in-class, production-grade models that materially move business metrics.
Why this role
- You will own our foundation model program end to end - from research direction to models running in production and driving real credit decisions.
- Your models directly move the numbers that matter: cost of risk, approval rates, and portfolio NPV across a fast-scaling credit portfolio.
- You'll work with one of the richest financial datasets in LATAM billions of transactions and behavioral events across 3M+ customers, growing daily.
Challenges that await you
- Build an end-to-end foundation model over financial event sequences - transactions, credit bureau data, and in-app behavioral events with subsequent fine-tuning for downstream business tasks: underwriting (PD), credit limit strategy, fraud detection, collections, and propensity models.
- Drive technical decisions end-to-end: methodology → implementation → performance and latency → robustness, interpretability, and regulatory compliance.
- Take models from research to production: training infrastructure, evaluation frameworks, model serving, latency/cost optimization, and monitoring.
- Research state-of-the-art approaches in the industry, publish your own work, and speak at leading conferences.
- Mentor senior engineers and scientists; own technical standards for model development across the team (design reviews, evaluation methodology, deployment practices).
- Communicate results clearly to cross-functional stakeholders: product, risk, business, and leadership.
What makes you a great fit
- Proven experience applying deep learning to sequential data - transformer architectures on event/transaction sequences strongly preferred, but not required.
- Strong foundation in mathematical statistics and probability theory.
- Deep understanding of machine learning algorithms (GBM, MLP, CNN, RNN, Transformers, etc.)
- Experience taking large models to production: distributed training, model serving, latency/cost trade-offs.
- Ability to strike a reasonable balance between solution complexity and practical applicability.
- Strong mathematical or technical education - degree in mathematics, physics, or CS from a top technical university
- Kaggle Competitions Master/Grandmaster or equivalent (a plus); experience developing models in banking or consumer lending (a plus)
- Strong communication skills.
Our ways of working
- Innovative Spirit: a commitment to creativity and groundbreaking solutions
- Honest Feedback: valuing open, transparent communication
- Supportive Team: a strong, collaborative community
- Celebrating Achievements: recognizing our wins together
- High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances
Our benefits
- Relocation support to one of our hubs - Mexico, Cyprus, Serbia, Spain with assistance for the employee and their family
- Flexible work from one of our offices or remote
- Healthcare coverage
- Education budget: language lessons, professional training and certifications
- Wellness budget: mental health and fitness activity reimbursements
- Vacation policy: 20 days of annual leave and paid sick leave
Originally posted on Himalayas