Unlock AI Potential: Data Visionary Role

2 semanas atrás


Brasil beBeeData Tempo inteiro US$35.000 - US$40.000

Job Opportunity:

We are seeking an exceptional Data Visionary to join our team and drive cutting-edge machine learning solutions that bridge research and practical deployment.

  • Develop Advanced CV Models: Design, train, and evaluate deep learning models tailored to complex visual data pipelines and novel problem domains.
  • Collaborate on Real-World Projects: Partner with software engineers and fellow data scientists to integrate research-driven models into deployable systems operating in dynamic production environments.
  • Client-Focused Problem Solving: Work closely with external stakeholders to frame ambiguous problems, explore solution paths, and translate technical insights into impactful results.
  • Explore, Iterate, Validate: Lead the exploration and analysis of large datasets using tools like Pandas, NumPy, and Spark to inform model design and evaluate performance under realistic constraints.
  • Research-Driven Innovation: Identify when state-of-the-art ideas can be adapted and productionized in service of the problem at hand.

Requirements:

  • A minimum of 2+ years of hands-on experience building computer vision models using modern deep learning techniques.
  • Proven ability to take models from prototype to production in Python-based workflows.
  • Experience working in client-facing roles or directly engaging with stakeholders to refine project requirements.

Necessary Skills:

  • Strong proficiency in Python and commonly used ML/data libraries (Pandas, NumPy, PyTorch, etc).
  • Familiarity with core computer vision tools and model types (YOLO, ResNet, segmentation models, optical flow, etc).
  • Experience working with large-scale datasets using distributed tools such as Spark.
  • Comfort navigating cloud environments (GCP, AWS, or similar); Databricks experience is a plus.
  • Solid grasp of experimental design, model evaluation, and data debugging practices.
  • Strong code hygiene: able to write clean, modular, testable code in collaborative environments.

Bonus (Nice To Haves):

  • Familiarity with MLOps practices (model tracking, versioning, reproducibility).
  • Experience contributing to multi-model systems or pipelines built by larger teams of data scientists and engineers.


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