National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Associate/Consultant, EMEA Life Science Strategy Consulting

IQVIA, Inc.
London
3 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer, Consulting

Principal Data Engineer, Consulting

Senior Data Scientist

Data Engineer Expert/Manager

Data Science and Analytics Senior Business Analyst

Data Engineering Associate

Location: EU Wide or London/Cambridge/Paris/Munich/Madrid/Basel and Amsterdam.

Real World Commercial Solutions to Create a Healthier World

In RWCS were passionate about improving the life sciences ecosystem and making decisions that accelerate innovation for a healthier world. Every day our team develops evidence-based strategies that are used in published research, clinical guidelines, and market access decisions, to improve healthcare at every touchpoint. We design and deliver Commercial Analytics, supported by our world-class proprietary data assets, solve complex client challenges through strategic consulting, and use leading edge market research techniques to support clients brand strategy, planning and measurement. Were purpose-driven problem solvers, that do what we love to make a greater impact on human health.

Commercial Strategy and Value & Payer Evidence

We are the global strategy arm of IQVIA and the worlds leading specialised advisor on critical business issues in the life sciences field. We apply creative research solutions to the worlds most pressing dynamic healthcare challenges. Our team is proud to offer end-to-end management consulting in key areas: brand strategy, portfolio analysis, launch excellence, commercial model design, digital health optimisation and Value & Payer Evidence.

Main Responsibilities:

  • AI and ML Solution Development: Develop AI and ML solutions to solve complex problems in healthcare and life sciences.
  • Data Analysis: Analyze large and complex data sets, identify trends and patterns, and generate insights that guide decision-making for clients.
  • Collaboration: Work with consultants, data engineers, and other data scientists to design and implement end-to-end data pipelines and analytical solutions.
  • Data Integrity and Security: Ensure the integrity, confidentiality, and security of client data in accordance with industry standards and regulations.
  • Communication: Communicate complex quantitative analyses clearly and precisely to non-technical stakeholders and clients.
  • Industry Knowledge: Stay updated with the latest developments in AI, ML, and other relevant technologies, and apply these advancements to improve services and offerings.
  • Client Interaction: Conduct interviews with senior managers and executives, contribute to client workshops, and build professional relationships with client team members.
  • Report and Presentation Preparation: Create sections of reports, presentations, and other client deliverables under supervision, and present findings to client audiences.
  • Research and Methodology Development: Conduct independent desk research and contribute to internal brainstorming sessions to develop project methodologies and recommendations.
  • Proposal Development: Assist in the development of proposals and client presentations, demonstrating the value of data science in solving business challenges in healthcare and life sciences.
  • Mentorship: Provide coaching and technical guidance to more junior team members.

Who are you?

  • You have a strong interest in using AI to make a difference in the life sciences and healthcare fields.
  • With a degree in a STEM subject or economics, you have the analytical skills needed to tackle complex problems.
  • Your experience includes working with data, data analytics, and AI technologies, allowing you to draw meaningful insights.
  • You think critically, always questioning the validity of your analyses to ensure accuracy.
  • You are naturally curious and enjoy finding innovative solutions to challenges.
  • Collaboration is important to you; you value learning from team members with diverse skills and backgrounds.
  • You are hands-on with technical tasks and comfortable communicating your findings to both colleagues and clients.

Desirable Technical Skills:

  • Coding knowledge: Proficiency in Python is preferred; familiarity with other programming languages (e.g., R, Java) is a plus.
  • Machine learning frameworks: Experience with frameworks such as TensorFlow, PyTorch, or scikit-learn.
  • Generative AI:Experience working with generative AI models, such as GPT, BERT, LLaMA, or other transformer-based architectures.
  • AI solution development: Experience in developing, testing, and deploying high-quality AI models and solutions.
  • Hands-on experience with large data sets: Proficiency in data manipulation, cleaning, and analysis.
  • Data visualization: Ability to create clear and insightful visualizations using tools like Matplotlib, Seaborn, or Tableau.
  • Cloud platforms: Familiarity with cloud services like AWS, Google Cloud, or Azure for deploying AI solutions.
  • Version control: Experience with version control systems like Git.
  • Technical communication: Strong ability to translate technical work and communicate quantitative insights effectively to a business audience.

Desirable Consulting Skills:

  • Experience in life sciences or healthcare:Familiarity with the unique challenges and requirements of working in the life sciences or healthcare sectors.
  • Communication: Excellent verbal and written communication skills to effectively convey complex technical concepts to non-technical stakeholders.
  • Teamwork: Ability to work collaboratively within a team, contributing to group projects and supporting colleagues.
  • Adaptability: Flexibility to adapt to new tools, technologies, and methodologies in a rapidly evolving field.
  • Problem-solving: Strong analytical and critical thinking skills to identify issues and develop innovative solutions.
  • Time management: Ability to manage multiple tasks and projects efficiently, meeting deadlines and prioritizing work effectively.
  • Attention to detail: Meticulous attention to detail to ensure accuracy and quality in all aspects of work.
  • Client-focused mindset: Understanding client needs and delivering solutions that meet their business objectives.
  • Continuous learning: Commitment to ongoing learning and professional development to stay current with industry trends and advancements.
  • Interpersonal skills: Strong interpersonal skills to build and maintain positive relationships with clients and team members.

In addition to the skills and experience above a Manager should have:

  • Bachelors degree or equivalent, graduate degree or MBA not required but a plus.
  • Fluency in English (spoken and written).
  • A willingness and ability to travel.
  • Right to live and work in the recruiting country.

Benefits:

We work hard to prioritise the things that matter most to you. Visit our benefits page for information on everything from perks to well-being initiatives and career enhancement.J-18808-Ljbffr

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.