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- Tasks: Lead the AI-powered manufacturing platform's computer vision capabilities and deliver production-grade AI systems.
- Company: Join a cutting-edge team focused on next-gen AI solutions.
- Benefits: Enjoy remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in AI-driven manufacturing with advanced technology.
- Qualifications: 7+ years in machine learning, strong computer vision expertise, and data engineering skills.
- Other info: Collaborate globally while leading innovative AI projects.
The predicted salary is between 72000 - 108000 £ per year.
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
- 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.
Key Responsibilities:
- 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
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.
Lead Data Scientist in London employer: Elios Talent
Contact Detail:
Elios Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to computer vision and data engineering. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts simply, as communication is key in cross-functional roles.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Lead Data Scientist in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the specific skills and experiences mentioned in the job description. Highlight your expertise in computer vision, data engineering, and any relevant projects you've worked on that align with our AI-powered manufacturing platform.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're the perfect fit for this role. Share your passion for AI and how your background in multimodal models and data pipelines can contribute to our mission at StudySmarter.
Showcase Your Projects: Include links to any relevant projects or portfolios that demonstrate your hands-on experience with Azure Machine Learning, Databricks, or any agentic AI frameworks. We love seeing real-world applications of your skills!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to lead our data science efforts.
How to prepare for a job interview at Elios Talent
✨Know Your Tech Stack
Make sure you’re well-versed in Azure Machine Learning, Databricks, and the frameworks mentioned like LangChain and LangGraph. Brush up on your experience with CNNs, transformers, and multimodal models, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built data engineering pipelines or deployed AI systems. Highlight your hands-on experience with feature extraction, automated training pipelines, and model observability. Real-world examples will demonstrate your expertise effectively.
✨Communicate Clearly
As a lead role, your ability to communicate complex ideas simply is crucial. Practice explaining your past work and technical concepts in a way that’s easy to understand for non-technical stakeholders. This will show your leadership potential and collaborative spirit.
✨Prepare for Cross-Functional Questions
Expect questions about how you would collaborate with product and engineering teams. Think of scenarios where you’ve influenced decisions or mentored others. Being able to articulate your leadership style and teamwork experiences will set you apart.
