QA Operations Shift Specialist

Dublin
11 months ago
Applications closed

Related Jobs

View all jobs

Summer QA Archivist Intern: Data Integrity & Archiving

Data Analyst, Reporting & Operations

Data Analyst, Reporting & Operations

Data Engineer

Technical Lead / Data Architect

Insight and Data Analytics Consultant – UK Part Time

QA Operations Shift Specialist

This is a shift role (4 x 12hr extended days followed by 4 days off)

The Quality Specialist provides direct Quality support to a production area as part of a Quality IPT (Integrated Product Team). With guidance from the Associate Director of Quality Operations, the Quality Specialist ensures quality and compliance of products manufactured by the functional area, adherence to Good Manufacturing and Documentation Practices, and represents quality on the shop floor. This is a Dublin based role within a leading Biotech multinational

Key Responsibilities

  • Responsible for review/approval of new and updated Master Batch Records / Electronic Batch Records

  • Review and approve production documentation such as executed electronic batch records and logbooks to ensure accuracy and compliance with cGMPs and company procedures

  • Provides presence on the shop floor to support compliance and data integrity

  • Review and approve new and updated SOPs/ Work Instructions and Controlled Job Aides

  • Actively participates in the Tier process and uses this forum to make issues visible and to partner with the functional area on resolution

  • QA support, review and approval of Commissioning and Qualification lifecycle documents for capital projects and new equipment.

  • Will serve as the Quality SME for Performance qualification (PQ) activities across the site including but not limited to: Equipment, Facility and Utility PQ studies, Cleaning Validation, SIP, process validation

  • Support sustaining activities such as Change Management, Deviations, CAPAs, Equipment Requalification and Periodic review, Site Maintenance & Calibration Program

  • Provides support to internal audits and regulatory inspections

    Required

  • Bachelor degree, in a scientific or engineering field (preferred); candidates with degrees in other fields will be considered if accompanied by significant relevant experience

  • Minimum 5 years of relevant post-degree work experience in GMP Manufacturing, Quality Assurance or Laboratory environment, Pharmaceutical/Biological Quality, Operations, Technical, or Regulatory function supporting manufacturing or laboratory operations.

  • This role requires a seasoned professional with the expertise with at least 5 years working knowledge in the biotech industry with specific understanding of QA operations an advantage as well as Regulatory agency engagement.

  • Evidence of leadership skills coupled with good oral and written communication skills

  • Understanding of cGMPs and of regulatory requirements as they apply to the pharmaceutical field or a related area

  • Demonstrated interpersonal skills including flexibility, collaboration and inclusion skills and ability to work in a team environment

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.