Commercial Procurement Manager

Bristol
9 months ago
Applications closed

Related Jobs

View all jobs

Commercial Data Analyst

Data Science Lead

Actuarial Data Scientist

Actuarial Data Scientist

Data engineers Cat 6

Junior Data Analyst

Our client, a global organisation urgently require an experienced Commercial Procurement Manager to join their team on a permanent basis.

In order to be successful, it is essential that you have the following experience:

Extensive commercial experience within big transformation programmes, within the Defence sector
Comprehensive commercial skills in negotiating contracts, writing strategy, forming industry partnerships and developing commercial packages
Experience within high value commercial procurement (either product or services)
SC Cleared

Within this role, you will be responsible for:

Drafting procurement and commercial strategies, including the development of credible option sets to support client decision making covering aspects such as commercial structures / lotting arrangements; payment, pricing and incentivisation mechanisms; route to market approaches and evaluation strategies
Input to support key market facing procurement documentation, including PQQs, ITN/ITPD, Heads of Terms, Contracts etc.
Generating analysis and insight: executing rigorous and insightful analysis to build an evidence base to support solution development and change
Contributing to negotiations between Defence/Public Sector and commercial suppliers
Developing contract management operating models, helping the client to define the necessary structures required to deliver the intended value from the commercial arrangements
Working in multi-disciplinary teams involved in solution design, business transformation and business case activities to support the develop of coherent procurement and commercial strategies that meet the overarching strategic intent
Working closely with clients and third parties (e.g. HMG suppliers) to develop strong professional relationships
Liaising with other internal teams (including our Digital Strategy, Innovation, Cyber Security and Data Analytics teams) to ensure the effective application of the subject matter expertise available, and a seamless client experience
Continuous Learning: proactively developing your own skills, building knowledge and sharing your insight with colleagues

This represents an excellent opportunity to secure a permanent role within a high profile organisation

People Source Consulting Ltd is acting as an Employment Agency 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.

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.