Remote opportunity at
BelvoSenior Applied Data Scientist
Belvo operates an open finance API platform focused on democratizing financial services access across Latin America by connecting financial innovators with legacy infrastructure. The organization is…
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About This Role
Belvo operates an open finance API platform focused on democratizing financial services access across Latin America by connecting financial innovators with legacy infrastructure. The organization is seeking a Senior Applied Data Scientist to function as the technical reference within the Intelligence Squad. This role centers on translating aggregated financial data and payment streams into production-ready decision models addressing fraud detection, risk scoring, categorization, and income verification. The position suits an…
Job Description
Belvo operates an open finance API platform focused on democratizing financial services access across Latin America by connecting financial innovators with legacy infrastructure. The organization is seeking a Senior Applied Data Scientist to function as the technical reference within the Intelligence Squad.
This role centers on translating aggregated financial data and payment streams into production-ready decision models addressing fraud detection, risk scoring, categorization, and income verification. The position suits an experienced practitioner who can shape modeling roadmaps, choose appropriate statistical or machine learning techniques, and manage models through production deployment and ongoing maintenance.
The successful candidate will collaborate closely with product, engineering, and business teams while utilizing advanced tooling including agentic coding frameworks and cloud infrastructure. The position requires a combination of strong statistical grounding, backend engineering competence, and practical experience delivering machine learning systems.
Responsibilities
- Design and lead statistical and machine learning models such as classification, regression, ranking, NLP, and LLM-based solutions for risk scoring, fraud detection, and transaction categorization
- Partner with product and business units to translate ambiguous business challenges into structured modeling problems and relevant feature engineering
- Deploy models into production environments and maintain ongoing ownership covering monitoring, performance drift, retraining, and retirement
- Design and execute experimentation frameworks including A/B testing and offline evaluation
- Contribute to the internal AI Chapter and collaborate with cross-functional partners across engineering, data research, and product
Requirements
- Have at least 5 years of hands-on professional experience as a Data Scientist, Applied Scientist, or in a comparable modeling capacity
- Demonstrate a strong background in statistics and the ability to justify modeling decisions with data
- Possess proficiency in Python for data science tools like pandas, scikit-learn, and at least one deep learning framework such as TensorFlow or PyTorch
- Show proficiency in SQL for extracting and shaping data
- Demonstrated experience taking models independently into production, including engineering fundamentals like testing, version control, and CI/CD
- Experience building applications with LLMs beyond basic API calls, incorporating tool use, agents, retrieval, or structured outputs into production
- Incorporate agentic coding tools into daily workflows
- Possess proficiency in written and spoken English
Qualifications
- Prior professional background in fintech, financial risk, or other regulated data environments
- Possession of a business or strategy background such as an MBA, consulting experience, or a product and strategy role
- Experience working in customer-facing data science capacities like forward-deployed engineering or applied data platform teams
- Familiarity with MLOps tools for model versioning, monitoring, and automated retraining
- Experience deploying machine learning models on AWS using managed services like SageMaker
- Experience writing evaluations or LLM-as-judge harnesses for non-deterministic systems
- Experience integrating LLMs directly into data pipelines for tasks like labeling, extraction, or categorization
Core Skills
Benefits
- Stock options
- Annual company bonus tied to company performance
- Flexible working hours
- Remote-friendly work arrangements
- Pet-friendly office environments
- Health insurance
- Paid time off on your birthday
- Laptop renewal every three years
- Training budget
- Team building events
- Bank holidays swap within 30 days
- Fitness and wellness stipends
- Yearly company offsites
- Weekly fresh fruit alongside unlimited tea and coffee
- Extra days off accrued upon completing two, three, four, and more years at the company
- Yearly department offsites
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the remote work policy for this role?
The posting indicates that the role is remote and remote-friendly, with optional offices available in São Paulo and Mexico City.
What are the primary experience requirements for applicants?
Candidates must have five or more years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role, along with production deployment experience and proficiency in Python and SQL.
Is salary information provided in the job posting?
No, salary details are not specified in the job posting.
What does the interview process involve?
The process includes a People team chat, a take-home challenge, a challenge presentation, a team fit chat, and a meeting with the founders.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.