QC Materials Analyst

London
9 months ago
Applications closed

Related Jobs

View all jobs

QC Data Analyst

QC Data Analyst

QC Analyst – GMP Lab & Data Integrity

Senior QC Specialist: Lead cGMP, Data Integrity & Investigations

Lead QC Specialist - cGMP, Data Integrity & Lab Leadership

Senior QC Lead — cGMP, Data Integrity & Investigations

QC Materials Analyst

Permanent

£30,000 - £35,000

Are you experienced in Quality Control and looking to make a real impact in a cutting-edge manufacturing environment? We are seeking a QC Materials Analyst to join a dynamic and fast-paced team supporting the release of innovative therapeutic products.

About the Role

As a QC Materials Analyst, you will play a key role in managing the inspection and release of incoming materials, ensuring full compliance with GMP standards. You’ll be central to maintaining a seamless supply chain that supports the manufacture of advanced therapeutic products.

Key Responsibilities

  • Perform inspection of incoming materials in accordance with SOPs and GMP requirements

  • Ensure materials meet specifications and are suitable for production use

  • Monitor inspection programmes and coordinate necessary testing

  • Maintain accurate documentation and control of material status

  • Support the readiness of multiple warehouse environments for material storage

  • Collaborate with internal teams to meet delivery timelines and maintain quality standards

  • Participate in deviation investigations and support continuous improvement initiatives

  • Ensure all work is carried out in compliance with GMP, Data Integrity, and Good Documentation Practices

    What We’re Looking For

  • Minimum 1 year of experience in a GxP-compliant environment

  • Familiarity with quality documentation and material inspection procedures

  • High attention to detail and strong problem-solving skills

  • Excellent organizational and communication abilities

  • Comfortable working across multiple functions and warehouse locations

  • Proficient in Microsoft Office applications

    Key Competencies

  • Strong sense of accountability and professionalism

  • Able to manage priorities and meet deadlines in a regulated environment

  • Passion for quality and process improvement

  • Comfortable contributing to team goals and engaging in planning activities

    This is a fantastic opportunity to grow within a forward-thinking, quality-driven environment. If you’re motivated by excellence and want to be part of something meaningful, we’d love to hear from you

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.