Senior Applied Data Scientist

New

BelvoOpen finance

Location: 🇧🇷 São Paulo / 🌎Remote Workplace: Remote, We ensure we’re always synced up for at least 3 hours per day.Full-TimeSenior

Salary not disclosed

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Job Details

Languages: You're proficient in written and spoken English.
Experience: 5+ years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role.
Required Skills: PythonSQLMachine LearningPyTorchPandasTensorflowCI/CDscikit-learn

Have 5+ years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role.
Have owned models end to end in production, from problem framing through live monitoring, and be able to describe measurable business impact.
Have a solid grounding in statistics and be able to explain how models work at that level.
Have built and compared multiple model types for the same problem and be able to justify the selected approach.
Be comfortable using Python for data science, including pandas, scikit-learn, and at least one of TensorFlow or PyTorch.
Use SQL to pull and shape data.
Have taken multiple self-built models into production.
Understand testing, version control, CI/CD, and maintainable code.
Have built LLM applications beyond API calls, including tool use or agents, retrieval, and structured outputs, and shipped at least one to production.
Have built an evaluation method for an LLM application.
Use agentic coding tools in daily work and be able to describe a specific workflow and its limitations.
Be proficient in written and spoken English.

Lead the design and selection of statistical and ML models for risk scoring, fraud detection, and transaction categorization.
Use agentic coding and research tooling to explore data, test feature hypotheses, scaffold pipelines, and build model services.
Work with Product and business teams to identify useful signals in financial data and define modeling problems.
Build or shape production pipelines, APIs, and services for models.
Monitor live models, manage drift and retraining, and retire models when evidence supports doing so.
Design and run A/B tests and offline evaluations to assess model changes.
Partner with Engineering, Data Research, Product, Intelligence Squad members, business stakeholders, and customers.
Explain modeling trade-offs and impact to non-technical audiences, including executives.
Keep current with GenAI releases, research, and tooling, and share useful findings through demos, documentation, or company sessions.
Determine when rules, heuristics, or improved data pipelines are preferable to modeling.

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