Regulatory Assistant, Compliance Assistant, Regulatory Affairs

London

9 months ago

Applications closed

Related Jobs

View all jobs

Senior Data Architect

Data Governance Lead

Data Analyst Senior Consultant, Assistant Manager, Manager - Belfast

Business Data Analyst

Data Governance SME

Senior Quantitative Risk Analyst

Employment Type: Full-Time

18 Months

1 day in London Office per week

£35-55k+ Benefits

4 days remote

We're shaping the future of health, technology, and human potential. We're seeking a Quality and Regulatory (QA/RA) Assistant to help drive our commitment to world-class innovation - with safety, compliance, and precision at its core.

Your Role:

You'll play a vital part in supporting our Quality Management System (QMS) and regulatory processes within the AI division. Under the guidance of the Senior Compliance Manager and Regulatory Advisor, you'll help us ensure we meet high standards, minimize risk, and drive forward life-changing AI-powered health solutions.

Key Responsibilities:

Provide administrative support for quality and regulatory activities, helping to ensure timely and accurate outcomes.

Collaborate closely with the Senior Compliance Manager and escalate issues or risks as needed.

Help coordinate with other subject matter experts across projects.

Attend and document project reviews, regulatory discussions, and quality assessments.

Manage documentation and support team members across engineering and compliance.

What We're Looking For:

Proven experience supporting Quality Management Systems (QMS) - ideally in healthcare or technology environments.

Excellent document management and data input skills.

Proficiency in Microsoft Office applications.

Strong interpersonal and written communication skills - you're someone who listens, clarifies, and gets things done.

Comfortable joining Microsoft Teams meetings to support reviews, take notes, and contribute to a quality-first culture.

Why Join Us?

Contribute to real-world health and AI solutions that have global impact.

Be part of a supportive team that values compliance, quality, and continuous improvement.

Engage directly with cutting-edge engineers and projects that are pushing the boundaries of what's possible in AI and healthcare.

People Source Consulting Ltd is acting as an Employment Business in relation to this vacancy. People Source specialise in technology recruitment across niche markets including Information Technology, Digital TV, Digital Marketing, Project and Programme Management, SAP, Digital and Consumer Electronics, Air Traffic Management, Management Consultancy, Business Intelligence, Manufacturing, Telecoms, Public Sector, Healthcare, Finance and Oil & Gas

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

Industry Insights

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

Feb 10, 2026

Careers

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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.