[Hiring] Data Scientist @Azumo

Role Description

Azumo is hiring a Data Engineer to own the layer everything else depends on: ingestion and transformation pipelines, storage and warehouse design, and the retrieval infrastructure that AI systems query. The role is fully remote across Latin America, aligned to your client's working day.

You will not be handed a specification. Azumo has shipped models into production since 2016, and the work here starts with a client who knows what bothers them and does not yet know what to measure. Turning that into a model, a baseline, and a number someone can act on is the job.

Where this role sits:

• The Data Engineer lane owns pipelines, storage, and the retrieval layer.
• The AI Engineer lane owns production behavior.
• The Software Engineer lane owns AI-augmented product delivery.
• This role is the Data Scientist lane, and it owns the question and the method.

One question places the boundary: when the output is wrong, whose problem is it?

• "The method was inappropriate, or the question was the wrong one" is yours.
• "The system did the wrong thing with an appropriate method" is the AI Engineer's.

Not quite your profile? Check our other openings:

• If you build the pipelines and retrieval layer models depend on — Data Engineer
• If you own how an AI system behaves in production — AI Engineer
• If you ship product software with agents in your toolchain — AI-Augmented Software Engineer
• If you've done all of the above and answered to the client directly — Forward Deployed Engineer

What you will build:

• The question: Translating a business decision into something measurable.
• Method and model: Supervised and unsupervised methods, deep learning with CNNs and Transformers in PyTorch.
• Fine-tuning and adaptation: Adapting pretrained and open-weight models with LoRA, QLoRA or PEFT.
• Measurement and experimental design: Baselines, holdouts, offline and online evaluation.
• Error analysis and handover: Telling apart a data problem, a label problem, a method problem and a framing problem.
• Work inside the client's environment: Their repositories, their standups, sometimes their customer calls.

How we work:

• Our engineers build with AI every day.
• We run an automated audit across the whole codebase on day one and every day after.
• We stay vendor-neutral across OpenAI, Anthropic, and open-weight models.
• We run Valkyrie, our own production layer, when a single interface to any model is the right call.

Qualifications

• 5+ years building and validating models against real decisions, with Python as your primary language.
• Depth in machine learning and deep learning: supervised and unsupervised methods, CNNs and Transformers, with hands-on PyTorch or an equivalent framework.
• Expert command of pandas, NumPy and scikit-learn.
• Strong foundation in statistics and experimental design.
• Fine-tuning or adaptation of pretrained models.
• Error analysis as a discipline.
• Cloud experience with AWS, GCP or Azure and their AI services.
• Active use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot.
• Clear written and spoken English, C1 or above.
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field, or equivalent professional experience.

Preferred Qualifications

• Evaluation of LLM systems: test-set design, model-as-judge or equivalent scoring.
• Open-weight fine-tuning and adaptation at depth: LoRA, QLoRA, PEFT.
• Multimodal work covering vision, speech, or document understanding alongside text.
• Causal inference, uplift modeling, or forecasting at scale.
• Experiment tracking and MLOps tooling: MLflow, Weights & Biases, Kubeflow or similar.
• Delivery under a compliance regime such as SOC 2 or HIPAA.
• Contributions to open-source AI libraries, published research or technical writing.

Benefits

• Paid time off (PTO)
• U.S. Holidays
• AI Training
• Mentored career development
• Profit sharing
• $US remuneration