Principal Statistician

JR United Kingdom
Leeds
4 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Engineer (GCP)

Principal Data Architect

Principal Data Analyst

Principal Data Engineer

Principal Data Engineer (MS Azure)

Social network you want to login/join with:

Client:

Warman O'Brien

Location:

Leeds, West Yorkshire, United Kingdom

Job Category:

Other

EU work permit required:

Yes

Job Views:

1

Posted:

22.08.2025

Expiry Date:

06.10.2025

Job Description:

Location: UK, France, or Germany (Flexible)

Are you a PhD-level statistician with experience in pharma and Health Technology Assessment (HTA)? Looking to apply your expertise to improve patient access on a global scale? This could be your next move.

Join a global statistics team dedicated to driving strategy and innovation in HTA, Patient Access, and Real-World Evidence (RWE). In this role, you’ll provide statistical leadership across evidence generation plans and payer strategies, collaborating with teams across geographies and functions.

What You’ll Do:

  • Lead statistical input for HTA submissions and access strategies
  • Apply advanced methods: economic modeling, network meta-analysis, indirect treatment comparisons
  • Collaborate with cross-functional teams on global evidence plans
  • Support payer submissions, responses, and scientific communication
  • Bring innovative statistical methods to life in real-world applications

What We’re Looking For:

  • PhD in Statistics (or MSc with significant experience)
  • 3+ years in pharma, HTA, health outcomes, or RWE
  • Strong programming skills (R, SAS) and statistical methodology knowledge
  • Excellent communicator with experience presenting to scientific and payer audiences
  • Confident managing projects and working across virtual/global teams

Why Apply?

  • Work on high-impact, global access strategies
  • Flexible EU location (UK, France, or Germany)
  • Collaborate with top-tier experts across functions
  • Be part of shaping real-world patient outcomes

If you're passionate about using data to accelerate patient access and thrive at the intersection of science, policy, and innovation — we want to hear from you.

Apply now or message us directly to learn more! Send your CV to [emailprotected] and receive more details surrounding this role and discuss this further!


#J-18808-Ljbffr

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.