Biostatistician II

ICON Strategic Solutions
Reading
3 weeks ago
Applications closed
Biostatistician II (Client dedicated | Fully remote)

ICON plc is a world‑leading healthcare intelligence and clinical research organisation. We foster an inclusive environment that drives innovation and excellence, and we welcome you to join us on our mission to shape the future of clinical development.


We are currently seeking a Biostatistician II to join our diverse and dynamic team. In this key leadership role under the supervision of senior biostatisticians, you will be responsible for the statistical aspects of clinical and pre‑clinical research projects. Your work will include assistance with case report form development, statistical analysis and interpretation of data, and reporting of results.


What You Will Be Doing

  • Assist senior biostatisticians in clinical study support, including study design, sample size calculations, patient randomisation, data analysis, and results reporting.
  • Support the statistical aspects of case report form design.
  • Help in writing statistical analysis plans, defining derived data, and designing statistical tables and figures for clinical summary reports.
  • Write data quality control specifications and utilize relevant computer languages and software.
  • Develop and execute data manipulation and analysis programmes.
  • Assist in conducting statistical analyses and interpreting results.
  • Prepare statistical summary reports and contribute to the statistical methods sections of integrated study reports.
  • Review and document analysis and programming work for audit trail purposes.
  • Contribute to project document standards and maintain project‑specific biostatistics files.
  • Collaborate effectively with ICON project team members, including data management, statistical programming, and clinical research personnel.
  • Embrace ICON's values, culture of process improvement, and commitment to client needs.

Your Profile

  • Bachelor’s degree in Statistics or a related field.
  • Experience in a biostatistics role in the pharmaceutical, clinical research, or healthcare industry is preferred.
  • Proficiency in statistical concepts, study design, sample size calculations, and patient randomisation.
  • Familiarity with statistical analysis plans and the design of statistical tables, figures, and data listings for clinical reports.
  • Strong data analysis skills, including data manipulation, retrieval, and interpretation.
  • Proficiency in relevant computer languages and statistical software packages.
  • Ability to write and implement programmes to select, retrieve, manipulate, edit, and analyse data.
  • Competency in assisting with statistical analyses and result interpretation.
  • Effective communication and collaboration skills to work with cross‑functional teams, including data management personnel, statistical programmers, and clinical research colleagues.

What ICON Can Offer You

In addition to a competitive salary, ICON offers a range of benefits designed to support well‑being, work‑life balance, and professional development. Our benefits are competitive within each country and reflect our commitment to a diverse, inclusive workplace.


Our Benefits Examples Include

  • Various annual leave entitlements.
  • A range of health insurance offerings to suit you and your family’s needs.
  • Competitive retirement planning offerings to maximise savings and plan with confidence for the years ahead.
  • Global Employee Assistance Programme (LifeWorks), offering 24‑hour access to a global network of over 80,000 independent specialised professionals who support you and your family’s well‑being.
  • Life assurance.
  • Flexible country‑specific optional benefits, including childcare vouchers, bike purchase schemes, discounted gym memberships, subsidised travel passes, health assessments, among others.

At ICON, inclusion & belonging are fundamental to our culture and values. We are dedicated to providing an inclusive and accessible environment for all candidates. ICON is committed to a workplace free of discrimination and harassment. All qualified applicants will receive equal consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.


If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process or to perform the essential functions of the position, please let us know or submit a request here.


Interested in the role, but unsure if you meet all of the requirements? We encourage you to apply regardless – there’s every chance you’re exactly what we’re looking for here at ICON.


If you are a current ICON employee, please click here to apply.


#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.