Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Clinical Data Scientist

NorthWest EHealth
Manchester
5 days ago
Create job alert

NWEH are recruiting a Clinical Data Scientist who will play a critical role in managing, analysing, and transforming clinical trial and real-world healthcare data. The Clinical Data Scientist will work as part of the Technical Team to support the delivery of validated data solutions through their work on study dataset generation, data quality assurance, statistical analysis, and clinical coding. This role requires expertise in clinical data standards and biostatistics, and experience of working within a regulated environment.


Responsibilities

  • Develop and maintain SDTM/CDISC compliant datasets.
  • Conduct data transformation, validation, and quality control for clinical study data.
  • Work with clinical coded data (SNOMED CT, MedDRA, WHO-DD) and electronic medical records (EMR) for real-world evidence (RWE) generation.
  • Provide expertise in terminology management and data mapping across various terminology schemas.
  • Contribute to data analytics, data management, and data quality initiatives to ensure high-integrity clinical datasets.
  • Collaborate with software engineers and data teams to integrate data pipelines into our platforms.
  • Ensure ongoing adherence to GCP, regulatory requirements, and industry best practices.
  • Explore and contribute to the emerging and appropriate application of AI and ML in NWEH products and services.
  • Provide statistical and data analytics expertise to internal teams and clients.

Essential Qualifications

  • Strong academic background with a good degree in a relevant field (e.g., Data Science, Biostatistics, Computer Science, Mathematics, Bioinformatics, or a related discipline) or relevant hands‑on experience in clinical data science, biostatistics, or statistical programming.
  • Experience with clinical trial dataset generation and standards.
  • Understanding of regulatory guidelines for clinical trials.
  • Proficiency in R, STATA or SAS for data analysis and statistical modelling.
  • Experience in clinical coding (e.g., SNOMED CT, ICD, MedDRA, WHO-DD).
  • Knowledge of real‑world data (RWD), and EMR/EHR integration.
  • Strong problem‑solving skills and the ability to work independently in a fast‑paced environment.
  • Excellent collaboration skills and experience working in a cross‑functional technical team.

Desirable Qualifications

  • Experience with SDTM dataset generation, CDISC standards.
  • Clinical data management experience.
  • Prior experience working in a regulated environment.
  • Experience working as part of an Agile/Scrum software product development team.
  • Experience using relevant cloud services, in particular services in Microsoft Azure.

Optional Certifications

  • NCCQ (UK) – National Clinical Coding Qualification
  • MedDRA Certification
  • CDISC SDTM Certification
  • SAS Certified Clinical Trials Programmer
  • ICH‑GCP Certification

At NWEH our focus isn't just about technology; it's about reshaping how clinical trials are designed and delivered in the UK and beyond.


Hours of work: Monday to Friday, 37.5 hours in total, flexible office hours are available.


#J-18808-Ljbffr

Related Jobs

View all jobs

Clinical Data Scientist

Data Scientist

Data Scientist | The Christie NHS Foundation Trust

Research Data Scientist | Barts Health NHS Trust

Research Data Scientist

Senior Data Scientist

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.