Senior Data Scientist

Montevideo - Uruguay

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Design develop and deploy end-to-end Computer Vision solutions using classical and deep learning approaches.
Build and optimize models for image classification object detection segmentation and OCR/document understanding.
Apply CNN architectures (e.g. ResNet EfficientNet ConvNeXt) and Vision Transformers (ViT Swin) including transfer learning and fine-tuning strategies.
Collaborate on data annotation strategies data augmentation pipelines and approaches to handle imbalance noise and domain shift.
Evaluate model performance using appropriate metrics (mAP IoU Dice ROC) and perform detailed error analysis.
Implement model explainability and interpretability techniques such as Grad-CAM and saliency maps.
Optimize models for production environments including quantization pruning and inference optimization (real-time vs batch).
Work closely with engineering teams on MLOps practices: monitoring drift data lineage and retraining loops.
Contribute to multimodal and GenAI initiatives including vision-language models vision in RAG systems and image-based prompting.
Ensure ethical AI practices considering privacy PII dataset bias and regulatory constraints.

Qualifications :

Strong foundations in Computer Vision including image formation color spaces and geometry.
Solid understanding of classical CV methods (filters edge detection feature extraction conceptually).
Hands-on experience with deep learning for vision and modern architectures.
Experience working with production ML systems and MLOps best practices.
Ability to communicate technical concepts clearly to both technical and non-technical stakeholders.
Strong problem-solving skills and ownership mindset.
Experience mentoring or guiding junior team members is a plus.

What about languages

Advanced English proficiency is required for effective communication with international teams and stakeholders.

Additional Information :

Our perks and benefits:

Every day lunches! (headquarters):

Vegetarian vegan gluten and sugar free options.
Gourmet meals every Friday with our on-site chef!

Flexible working options to help you strike the right balance.

All the equipment you need to harness your talent (Macbook and accessories).

Snacks and beverages available everyday (headquarters).

After office events football tennis and game nights (headquarters).

Everyone is welcome to join our football league every Wednesdays and Fridays.

Challenge your teammates to a pool game and win the offices trophy! Tennis courts available for friendly matches.

Not a sports person Dont worry we also have chess championships game and music nights for you to join!

Learning opportunities:

AWS Certifications (we are AWS Partners).
Study plans courses and other certifications.
English Lessons.
Learn from your teammates on our Tech Tuesdays!

Mentoring and Development opportunities to shape your career path.

Anniversary and birthday gifts.

Great location and even greater teammates!

So what are the next steps

Our team is eager to learn about you! Send us your resume or LinkedIn profile below and well explore working together!

Remote Work :

Yes

Employment Type :

Full-time

Design develop and deploy end-to-end Computer Vision solutions using classical and deep learning approaches.Build and optimize models for image classification object detection segmentation and OCR/document understanding.Apply CNN architectures (e.g. ResNet EfficientNet ConvNeXt) and Vision Transform...

Design develop and deploy end-to-end Computer Vision solutions using classical and deep learning approaches.
Build and optimize models for image classification object detection segmentation and OCR/document understanding.
Apply CNN architectures (e.g. ResNet EfficientNet ConvNeXt) and Vision Transformers (ViT Swin) including transfer learning and fine-tuning strategies.
Collaborate on data annotation strategies data augmentation pipelines and approaches to handle imbalance noise and domain shift.
Evaluate model performance using appropriate metrics (mAP IoU Dice ROC) and perform detailed error analysis.
Implement model explainability and interpretability techniques such as Grad-CAM and saliency maps.
Optimize models for production environments including quantization pruning and inference optimization (real-time vs batch).
Work closely with engineering teams on MLOps practices: monitoring drift data lineage and retraining loops.
Contribute to multimodal and GenAI initiatives including vision-language models vision in RAG systems and image-based prompting.
Ensure ethical AI practices considering privacy PII dataset bias and regulatory constraints.