R/SQL Developer in Clinical Trials

PSI CRO
Oxford
2 months ago
Applications closed

Related Jobs

View all jobs

Lead BI Developer - Tableau and PowerBI - Consulting

Senior API Developer (Python & AWS)

Data Science Consultant- Architecture Focus

Lead Data Scientist[975963]

Data Engineer

Data Scientist - active NPPV3 required

Job Description

Actual Position name: Clinical Data Scientist

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 years of 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 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.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 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.