Senior Biostatistician

AL Solutions
Liverpool
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - (Python & SQL)

Senior Data Governance Analyst

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Data Engineer

Senior Data Engineer

AL Solutions are working in partnership with a fast emerging Canadian CRO that have recently began the next phase of their global expansion having recently opened a new affiliate in the UK as part of their long-ter m European growth project.


We are currently looking for a Senior Biostatistician to join them on this journey be one of the first employees within the European team. With their strong commitment to growing this UK team and further expansion into Eastern Europe, my client have excellent progression and development paths in place which has enabled them to achieve a staff retention rate of 94% in recent years.


Responsibilities:


  • Manage assigned projects from planning through delivery, building strong client relationships
  • Provide statistical input for study design, analysis, and reporting.
  • Write protocol statistics sections, calculate sample sizes, and prepare randomization plans and codes.
  • Develop Statistical Analysis Plans (SAPs) and respond to peer review feedback.
  • Maintain QC/QA documentation and support development of new statistical methods and processes.
  • Develop and/or review ADaM dataset specifications. Review and provide input to SDTM dataset specifications when required.
  • Conduct statistical analyses according to the SAP and address review comments
  • Prepare statistical content for CSRs, publications, abstracts, and presentations.
  • Review data management plans and validation specs


Required Experience / Skills:


  • Degree in Statistics or related fields
  • Strong experience working as a biostatistician within clinical trials
  • Previous experience independently leading studies
  • Proficient with SAS
  • Good working knowledge with CDISC, ADaM and SDTM


For more details, please contact Jack Kavanagh at AL Solutions or submit your application.


Jack Kavanagh

AL Solutions

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.