Associate Principal Scientist, QSP

TN United Kingdom
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer, Associate Director, London

Postdoctoral Research Associate in Machine Learning - Durham

AI & Data Engineer - KTP Associate

Data Engineering Associate

Lead Data Developer

Lead Data Engineer

Social network you want to login/join with:

Associate Principal Scientist, QSP

Client:Certara

Location:United Kingdom

Job Category:Other

EU work permit required:Yes

Job Reference:0143c01cec68

Job Views:7

Posted:13.02.2025

Expiry Date:30.03.2025

Job Description:

Overview

About Certara

Certara is a growing company that provides a dynamic and exciting place to work. Our purpose is to assist in accelerating the development of meaningful medicines that make an impact on our society and the people that need them most. Innovation and creativity are highly valued, and everyone is given the opportunity for training and continuous development. Our portfolio spans the discovery, preclinical, clinical and post-marketing phases of drug development, working with 1,200 commercial companies, 250 academic institutions, and numerous regulatory agencies.

The Certara Quantitative Systems Pharmacology (QSP) group is a global organization focused on the impactful application of QSP and mechanistic PKPD modeling and simulation in drug discovery and development. We are leading several first-of-its-kind QSP Consortia with more than 10 global pharmaceutical companies and have a rapidly expanding consultancy portfolio. Our QSP scientists work in close collaboration with other Certara staff and clients to generate and use state-of-the-art mechanistic models for decision making in pharmaceutical R&D. The models are designed in close dialogue with clients and developed with a view to creating user-friendly tools that will inform drug development programs from discovery to regulatory filing. We have unique capability to seamlessly integrate bespoke QSP models into the Simcyp physiologically based pharmacokinetic (PBPK) platform. Besides our well-established leadership in applied QSP approaches in Immunology and Immuno-Oncology, we are now also the leading provider of QSP approaches in neuro-degenerative diseases through Certara’s recent acquisition of the In Silico Biosciences modelling platform.

We are seeking highly motivated and talented QSP modelling experts at all levels, who share our passion for using quantitative approaches to understand and impact complex drug discovery and development questions. Positions are available at various levels of experience across our global locations and for flexible, home-based roles.

Responsibilities

Technical

  1. Postdoctoral experience in systems modelling for drug discovery/ development teams.
  2. PhD (or equivalent) in life sciences, engineering, computer sciences, mathematics, physics or equivalent.
  3. Good understanding of PKPD, pharmacology and biology and its application in drug discovery and development.
  4. Very familiar and outstanding hands-on expertise with Matlab, Neuron or other similar modelling software.
  5. Strong publication record (commensurate with level of experience).

Team working

  1. Excellent interpersonal skills.
  2. Skilled and experienced at facilitating a multi-disciplinary team to work together on a complex project.
  3. Ability to communicate with all stakeholders clearly and efficiently.
  4. Organised and with track record of delivering quality results on time.

Personal

  1. Ability to work virtually with project teams across different locations and time zones.
  2. Leads by example.
  3. Self-reliant.

Other skills

  1. Highly computer literate and familiar with modern IT and communications technology.
  2. Good presentation skills.
  3. Combines scientific excellence with ability to make impact in drug discovery and development programs.
  4. Ability to manage multiple client project timelines and deliverables simultaneously.

Role (depending on experience)

  1. Overall responsibility for project delivery of quality QSP solutions on time.
  2. Provide hands-on technical support for model building when required.
  3. Work with clients to ensure they receive the expected added value.
  4. Work with the consortium/ consulting teams to ensure optimal deployment of resources.
  5. Work with Certara QSP line management to deliver optimum solutions.
  6. Provide general expert technical input across Certara QSP portfolio.
  7. Mentor other Certara QSP staff.

Qualifications

  1. Postdoctoral experience in systems modelling for drug discovery/ development teams.
  2. PhD (or equivalent) in life sciences, engineering, computer sciences, mathematics, physics or equivalent.
  3. Good understanding of PKPD, pharmacology and biology and its application in drug discovery and development.
  4. Very familiar and outstanding hands-on expertise with Matlab, Neuron or other similar modelling software.
  5. Strong publication record (commensurate with level of experience).

Certara bases all employment-related decision on merit, taking into consideration qualifications, skills, achievement, and performance. We treat all applicants and employees without regard to personal characteristics such as race, color, ethnicity, religion, sex, sexual orientation, age, nationality, marital status, pregnancy, physical or mental condition, genetic information, military service, or other characteristic protected by law.

#J-18808-Ljbffr

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.

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!

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.

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.