Chief Machine Learning Officer

Há 4 dias

São Paulo, São Paulo, Brasil beBeeMachineLearning Tempo inteiro R$200.000 - R$240.000
Job Role

The primary focus of this role is to delve into the realm of machine learning, driving innovation and pushing the boundaries of what is possible.

As a member of our team, you will be part of an exploration squad within the AI department, where we take a step back from day-to-day urgencies and pursue ambitious projects with high impact potential.

You will join our efforts to discover and refine transformer-based neural network architectures, data pipelines, pre-training objectives, and fine-tuning strategies that will power the next generation of machine-learning models for finance.

  • Key Responsibilities:
  • Design and Develop machine learning models to drive business growth and improve financial outcomes.
  • Collaborate with cross-functional teams to identify areas of improvement and develop solutions to meet business needs.
  • Stay Up-to-Date with the latest advancements in machine learning and apply new techniques to drive business results.
Requirements

This role requires a deep understanding of machine learning concepts, as well as experience with large-scale data processing and analysis.

Additionally, you should have a strong track record of delivering high-quality results and collaborating effectively with others.

  • Essential Skills:
  • Deep ML intuition. You think naturally in embeddings, matrices, tensors, projections, gradients, and loss landscapes—and you can explain those ideas without a whiteboard meltdown.
  • End-to-end modeling skill. From data wrangling to metric-driven deployment, you spot promising ML opportunities and turn them into working systems.
  • Fluency in Python (and friends). PyTorch, TensorFlow, NumPy, pandas, SQL—you're productive across the modern ML stack and pick up new tools quickly.
  • Data-driven detective work. You're comfortable sifting through terabytes of noisy data to surface the patterns that matter.
  • Research mindset. Reading arXiv before breakfast, reproducing baselines, and adapting fresh papers to real problems feels like fun, not homework.
  • Parallel-experiment discipline. You're organized enough to run and track multiple training jobs at once without losing your mind (or your metrics).

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