Lead Biostatistician

Planet Pharma
Bristol
8 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (AWS & Snowflake)

Data Lead - Data Transformation Programme

Planet Pharma is seeking a talented and motivated Biostatistician to support the design, analysis, and reporting of clinical trials and observational studies. This role offers the opportunity to contribute to meaningful research while developing expertise in statistical methodology and regulatory standards.


You will work under the guidance of senior statisticians to execute statistical tasks with increasing independence. You’ll also serve as a Methodology Statistician, validating routine techniques and identifying opportunities for innovative approaches.

This position is ideal for someone who is detail-oriented, collaborative, and eager to grow within a dynamic research environment.


Key Responsibilities

  • Contribute to the planning, execution, and reporting of clinical and observational studies, including post hoc, HTA, regional, PK-PD, and biomarker analyses.
  • Author and review key study documents such as protocols, statistical analysis plans (SAPs), CRFs, data validation plans, and TLF specifications.
  • Perform statistical analyses in accordance with protocols, SAPs, good statistical practice, and regulatory guidelines.
  • Collaborate with cross-functional study teams (e.g., programmers, data managers, clinical leads) and provide statistical insights on study design and methodology.
  • Engage with medical literature and industry developments to broaden expertise beyond statistics.
  • Support the development of clinical study reports and publications by interpreting statistical results.


Required Experience & Skills

  • PhD with some experience, or Master’s degree in Biostatistics with substantial experience in biomedical research, pharmaceutical, CRO, academic, or healthcare settings.
  • Proficiency in R is essential; knowledge of SAS is preferred.
  • Experience in oncology and clinical trial methodology is highly desirable.

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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.