Lead Data Scientist

Location: United Kingdom (Remote)

Seniority: Lead / Principal-Level

Platform-Defining 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-grade AI systems.

Modern AI Stack: Azure Machine Learning, Databricks, multimodal models, and agentic AI frameworks.

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 proven track record of delivering production AI systems using Azure Machine Learning, Databricks, and agentic AI frameworks such as LangChain and LangGraph. This is a highly cross-functional, hands-on leadership role and is foundational to the platform’s intelligence layer.

• 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 TensorFlow.
• Background applying computer vision to real-world imagery, such as inspection, materials identification, part recognition, or manufacturing-related data.
• Experience in additive manufacturing is not required — applied vision experience is key.

Data Engineering for AI:

• Demonstrated ability to build data pipelines that support ML workflows, including:

• Feature extraction and embedding generation.
• Schema and metadata alignment.
• Feature engineering and feature store integration.
• Automated data validation and drift checks.

Databricks & Distributed Processing:

• Proficiency in SQL and PySpark.
• Experience using distributed compute patterns to process large image and metadata datasets.

Agentic AI & Orchestration:

• Familiarity with LangChain, LangGraph, or similar frameworks for building tool-using AI agents and orchestrating multi-step workflows.

Model Observability & Drift Detection:

• Experience implementing telemetry, monitoring pipelines, and drift detection using Application Insights.

Software & Data Foundations:

• Solid understanding of APIs and microservices.
• Experience with structured and unstructured data modeling.
• Ability to produce reproducible, production-ready ML workflows.
• Experience in manufacturing, industrial automation, or mechanical engineering domains.
• Experience processing 3D or geometric data (CAD files, point clouds, meshes, depth imagery).
• Familiarity with vector databases or embedding-based search systems for multimodal reasoning.
• Experience optimising models for performance, latency, and cost in production.
• Understanding of secure ML development practices aligned with NIST 800-53 or similar standards.

Prior leadership experience mentoring data scientists and collaborating closely with data and platform engineers.

Education: Master’s or Ph.D. preferred in Computer Science, Data Science, Engineering, or a closely 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 systems.

Soft Skills: Excellent communication and architectural thinking; Ability to influence engineering and product stakeholders.

High-Impact Leadership: Own the intelligence layer powering AI-driven manufacturing decisions.

Advanced AI Challenges: Multimodal reasoning, vision + metadata fusion, agentic AI workflows.

UK Remote Role: Work remotely while collaborating with global engineering and product teams.

Hands-On Leadership: Strategic ownership with real technical depth and execution.

We partner with innovative teams building next-generation AI platforms that solve complex, real-world problems. Our work focuses on production-ready AI, strong data foundations, and close collaboration across data science, engineering, and product. We value ownership, clarity, and measurable outcomes.