Market Research and Insights Partner

Slough
8 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst – Payments Consulting

Data Analyst – Payments Consulting

Data Analyst

Strategy Data Analyst Level 4 Apprentice

D2C Data & Business Intelligence Manager

D2C Data & Business Intelligence Manager

CK Group are recruiting for a Market Research and Insights Partner, to join a global pharmaceutical company, based in Slough, on a contract basis, initially for 3 months. This role can be fully remote.

Salary:

Inside IR35. £34.75 per hour (PAYE) or £47.31 per hour (Umbrella). Flexible for experienced candidates. 

Market Research and Insights Partner:

This position will lead primarily within the immunology portfolio, but there may be the opportunity to work with other therapy areas, according to need.
Planning and leading agreed market research (across PVUs and stakeholders, with a planned primary allocation); ensuring that HCP, patient, and payer insights are at the heart of brand strategies / tactics, e.g., campaigns.
Ensuring all Market Research complies with the Pharmaco-Vigilance Safety Reporting obligations and ensures contracts contain the right PV clauses, vendors receive the right PV training and execute correctly on their PV obligations.
Taking joint leadership on the management of the agreed market research activities, spear-heading new and emerging research methodologies if appropriate.
Working closely together with the regional analytics teams in either US and/or Europe to ensure the right use of existing data sources support to answer business questions in the most effective way.
Your Background:

Successful track record in previous market research roles within pharma industry (agency or client side) for at least 7 years.
Strong understanding of relevant commercial and medical data, and primary market research methods and applications.
Ability to advise on stakeholder insights needs through a deep understanding of the business and knowledge of relevant available insights.
Strong quantitative, qualitative and interpretation skills, and you are a structured problem solver. 
Company:

Our client is a global biopharmaceutical company, focused on creating value for people living with severe diseases in immunology and neurology now and into the future.

Location:

Hybrid in Slough or fully remote.

Apply:

It is essential that applicants hold entitlement to work in the UK. Please quote job reference (Apply online only) in all correspondence. 

Please note: 

This role may be subject to a satisfactory basic Disclosure and Barring Service (DBS) check.

If this position isn't suitable but you are looking for a new role, or if you are interested in seeing what opportunities are out there, head over to our LinkedIn page (cka-group) and follow us to see our latest jobs and company news.

INDKA

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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.