Clinical Data Scientist

PSI CRO
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Director, Quantitative Imaging or Imaging Biomarkers (London Area)

Director, Quantitative Imaging or Imaging Biomarkers (City of London)

Director, Quantitative Imaging or Imaging Biomarkers

Director, Quantitative Imaging or Imaging Biomarkers

Data Engineer

Job Description

Reporting to the Clinical Data Science Manager, the Clinical Data Scientist is an integral part of our team here at PSI. You will work with clinical trials patient and operational data, develop new data solutions and set up Risk-based Monitoring systems in Process Improvement department.

Hybrid work in Oxford

  • Participate in selection of the Risk-Based Monitoring (RBM) system and provide relevant training to the project team and/or Sponsor
  • Set up and maintain RBM systems, collaborating with the Central Monitoring Manager
  • Manage complex datasets from multiple sources, including data extraction, transformation, and loading into PSI data platform
  • Program and produce data listings, tables, and figures for Clinical Data Reviewers and Central Monitoring Managers
  • Calculate Key Risk Indicators and Quality Tolerance Limits, applying advanced analytical techniques to identify data trends for Centralized Monitoring
  • Collaborate cross-functionally to identify study challenges and develop data solutions using advanced analytics
  • Communicate data findings and solutions to stakeholders effectively
  • Contribute to the development of databases, software products, processes, and Quality System Documents for Centralized Monitoring


Qualifications

Must have:

  • Degree in Data Science, Mathematics, Statistics, Computer Science or equivalent; or relevant work experience and professional qualifications
  • At least 5 years of experience in Data Management, Biostatistics, and Centralized Monitoring
  • At least 4 years of experience in one or more of the following: R, R Shiny, SAS, SQL and associated packages and libraries
  • At least 2-year experience in data engineering area including one or more of the following: relationship databases, data warehousing, data schemas, data stores, data modeling, testing, validation and analysis
  • Full professional proficiency in English
  • Strong analytical an logical thinking
  • Communication and collaboration skills

Nice to have:

  • Experience with CluePoints RBM system
  • Knowledge of statistical methods and techniques for analyzing data
  • Experience with using Machine Learning technics and products testing and validation



Additional Information

What we offer:

  • We value your time so the recruitment process is as quick as 3 meetings
  • We'll prepare you to do your job at highest quality level with our extensive onboarding and mentorship program
  • You'll have excellent working conditions - spacious and modern office in convenient location, and friendly, supportive team who love to hang out together 
  • You'll have permanent work agreement at a stable, privately owned company
  • We care about our employees - aside from competitive salary, you'll have good work-life balance with flexible working hours and additional days off, life and medical insurance, sports card, lunch card 
  • We're constantly growing which means opportunities for personal and professional growth 

Make the right call and take your career to a whole new level. Join the company that focuses on its people and invests in their professional development and success.

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.