Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data & Machine Learning Architect

Nibley
2 days ago
Create job alert

Job Title: Lead Data & Machine Learning Architect
Location: Yate (On-Site 2 Days Per Week)
Salary: Up to £60,000 (Depending on Experience) 

Overview of Business:

This is a chance to join a team who are creating an Energy Management Solutions to minimise energy cost & increase higher returns from energy assets. 

We are seeking a highly skilled Lead Data & Machine Learning Architect to design and lead the development of robust data and machine learning systems.

You will take ownership of the data architecture, drive the development and deployment of machine learning models, and contribute to the strategic use of data across the organisation.

Key Responsibilities:

Data Architecture
Schema & Model Ownership: Design, implement, and maintain logical and physical data models, primarily using PostgreSQL.
Data Integration: Build and manage robust data pipelines to ingest, clean, and unify data from APIs, sensors, and other external sources, using tools like Dagster.
System Design: Select appropriate storage and processing technologies tailored to system needs.
Governance & Security: Define and enforce data governance policies, ensuring compliance with standards such as GDPR.
Performance & Scalability: Ensure data infrastructure is optimised for performance and can scale with growing data demands. Machine Learning
Model Development: Lead the development of machine learning models, particularly for time-series forecasting (e.g., predicting on-site energy production).
Data Preparation: Manage the transformation and preparation of datasets for model training and evaluation.
Experimentation: Design and execute experiments, tune hyperparameters, and iterate on models to improve performance.
Deployment & Monitoring: Deploy models into production environments and monitor their ongoing performance.
Maintenance: Establish retraining workflows and manage model updates as systems and data evolve. Cross-functional Collaboration
Project Engagement: Work closely with project managers and stakeholders to align data and ML capabilities with new features and strategic initiatives.
Requirements Gathering: Collaborate with business teams to define clear, actionable requirements for data pipelines, storage solutions, and ML workflows. If you are interested, please apply with your latest CV and we will be in touch

Related Jobs

View all jobs

Lead Data & Machine Learning Architect

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Scientist – NLP

Lead Data Scientist - Customer Development

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.