Quality Co-ordinator

Sheffield
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Assistant Data Analyst

Data Engineer - Highly competitive salary

Data Quality Analyst

Data Quality Analyst

Data Engineer - Contract - 9+ Months

Sue Ross Recruitment are seeking a Quality Co-ordinator to join a leading provider of debt recovery services across the UK, employing over 450 people nationwide. This is a fantastic opportunity to play a key role in monitoring, improving, and maintaining call quality standards while helping to develop the call centre team. If you have a strong regulatory background, coaching experience, and a keen eye for quality and compliance, we’d love to hear from you!

The Role:

Working closely with contact centre managers, you will be instrumental in ensuring quality assurance across all customer interactions. You will provide structured feedback and coaching to call agents, helping them refine their approach, improve compliance, and enhance overall customer service.

Your role will involve monitoring calls, analysing key performance metrics, conducting training sessions, and supporting compliance and ISO audits. You will also play a key part in data accuracy and process improvements, helping to drive efficiency across the business.

Key Responsibilities:

  • Monitor and assess call quality, ensuring high standards of customer service and compliance.

  • Provide coaching and feedback to call centre team members to enhance their performance.

  • Conduct one-to-one coaching sessions, identifying strengths and areas for improvement.

  • Assist in managing KPIs, ensuring team focus on quality, call handling time, and collection outcomes.

  • Ensure adherence to regulatory and compliance guidelines, protecting both customers and clients.

  • Conduct online testing and monitor agent performance standards.

  • Support improvements in data quality entered into our systems, providing recommendations for change.

  • Assist in training new and existing team members, building their confidence and capability.

  • Collaborate with clients and internal teams to ensure smooth operations and compliance.

  • Support ISO internal and external audits, ensuring all quality standards are met.

    The ideal candidate will have previous experience working within a regulated industry and must be proficient using a variety of IT systems.

    Previous experience in a telephony environment is required, and you must be able to assess call handling performance and identify areas for improvement.

    In return, our client offers flexible working hours (between 8am and 8pm Monday – Friday), full training and career development opportunities and a competitive starting salary. On-site parking and daily refreshments are also provided.

    Unfortunately due to the number of applications we receive, we are unable to provide individual feedback to all applicants. Please assume that if you do not hear from us within 72 hours that your application has been unsuccessful on this occasion.

    May we take this opportunity to thank you for expressing an interest in one of our roles and wish you the very best in your search for employment

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.