Statistical Programmer

Planet Pharma
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior DSX Data Scientist

Senior DSX Data Scientist

Senior Data Engineer

Assistant Professor in Statistical Data Science

Assistant Professor in Statistical Data Science

Planet Pharma is pleased to be recruiting for a Statistical Programmer to work on a full-time permanent basis for a leading, global organisation in the UK.


***Please note you must reside in the UK***


Ideally we are seeking someone with proven experience of working as a Statistical Programmer within thepharmaceuticalindustry.


You should also hold the following;

  • Bachelor’s or Master’s in Statistics, Mathematics, or related field or equivalent.
  • Substantial experience in clinical data standards ADAMS, TLFs, and submission guidelines.
  • Hands-on programming experience within one or more statistical/data science programming languages (e.g., R, SAS, or Python). This includes writing code to manipulate data and analyse a wide array of data sources/types.
  • Knowledge of the data science lifecycle and process flow (e.g., ETL, data quality, statistical data analysis, machine learning, data randomization.
  • Knowledge of study documents such as Protocol, SAP, TLF specs, and data specification.
  • Oncology experience is 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.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 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.