Lead Data Scientist

Lead Data Scientist – Computer Vision & AI Platforms

Location:

LATAM (Remote)
Seniority:
Lead / Principal-Level
Focus Area:
Computer Vision
· AI Platforms
· Manufacturing Intelligence
Role Highlights
Foundational AI Role:
Lead the computer vision intelligence layer of an AI-powered manufacturing platform
End-to-End Ownership:
Combine vision modeling, data engineering, and MLOps to deliver production AI systems
Modern AI Stack:
Azure Machine Learning, Databricks, multimodal models, and agentic AI frameworks
Summary
We are seeking a Lead Data Scientist to drive the computer vision capabilities of an AI-powered manufacturing intelligence platform. This role blends advanced vision modeling, multimodal reasoning, and data engineering to assess manufacturability and printability from images, schematics, and part metadata.
The ideal candidate brings deep applied computer vision expertise, strong hands-on experience building data engineering pipelines for ML workflows, and a track record of delivering production-grade AI systems using Azure Machine Learning, Databricks, and agentic AI frameworks such as Lang Chain and Lang Graph.
This is a highly cross-functional, hands-on leadership role and is foundational to the platform's intelligence layer.
Required Skills & Experience
Computer Vision & Deep Learning
Strong experience building, training, and evaluating CNNs, transformers, or multimodal models
Use cases including image classification, feature extraction, defect detection, and segmentation
Proficiency with PyTorch and/or Tensor Flow
Industrial / Applied Vision
Background applying computer vision to real-world imagery, such as inspection, materials identification, part recognition, or manufacturing-related data
Additive manufacturing experience not required — applied vision experience is key
Data Engineering for AI
Demonstrated ability to build data pipelines that support ML workflows, including:
Large-scale image ingestion
Feature extraction and embedding generation
Schema and metadata alignment
Delta Lake pipelines
Feature engineering and feature store integration
Automated data validation and drift checks
Azure ML & MLOps
Hands-on experience deploying, monitoring, and managing models using Azure Machine Learning

Experience with :
Model versioning
Batch inference jobs and online endpoints
Environment management
Automated training pipelines
Databricks & Distributed Processing
Proficiency in SQL and Py Spark

Experience with distributed compute patterns for large image and metadata datasets
Agentic AI & Orchestration
Familiarity with Lang Chain, Lang Graph, or similar frameworks for tool-using AI agents and multi-step workflows
Model Observability & Drift Detection
Experience implementing monitoring and drift detection using:
Azure ML Monitoring
Application Insights
Custom statistical techniques
Software & Data Foundations
Strong Python engineering skills
Solid understanding of APIs and microservices

Experience with structured and unstructured data modeling
Ability to produce reproducible ML workflows
Preferred Skills & Experience
Experience in manufacturing, industrial automation, or mechanical engineering domains
Experience working with 3D or geometric data (CAD files, point clouds, meshes, depth imagery)
Familiarity with vector databases or embedding-based search systems for multimodal reasoning
Experience optimizing models for performance, latency, and cost in production
Understanding of secure ML development practices aligned with NIST 800-53
Prior leadership experience mentoring junior data scientists and collaborating with data and platform engineers
General Requirements

Education:

Master's or Ph.D. preferred in Computer Science, Data Science, Engineering, or related field

Experience:

7+ years in machine learning or applied data science
3+ years focused on computer vision
3+ years building data engineering pipelines for ML

Soft Skills:

Excellent communication and architectural thinking
Ability to influence engineering and product teams
Comfortable operating in a fast-paced, iterative delivery environment
Why Join Us
Platform-Defining Role:
Own the intelligence layer powering AI-driven manufacturing decisions
Advanced AI Challenges:
Multimodal reasoning, vision + metadata, agentic workflows
Modern

Infrastructure: Azure ML, Databricks, distributed compute, production MLOps
Remote LATAM Opportunity:
Work remotely while collaborating with global product and engineering teams
Leadership & Impact:
Shape technical direction while remaining deeply hands-on
About Us
We work with innovative teams building next-generation AI platforms that solve complex, real-world problems. Our approach emphasizes production-ready AI, strong data foundations, and close collaboration across product, engineering, and data science. We value ownership, clarity, and measurable impact.