AI Engineer

Machine Learning Engineer

Commercial Solutions

This is an exciting opportunity to work in one of the world's leading human data science teams working with Commercial Solutions to help our clients answer specific questions globally, make more informed decisions, and deliver results. We are looking for an experienced and highly productive Machine Learning Engineer to join our team.

The team develops software primarily in python, interspersed with a variety of other technologies, including tooling around CI / CD and ML Ops (and anything you can introduce which simplifies or improves our processes is always welcome). The team creates and maintains packages that support ML engineering projects. As such, the team does not directly develop models but instead writes modeling packages and works closely with the model building team as they use the packages for client delivery.

Please don’t feel you need to perfectly match the requirements; we are trying to give a flavor of what we do, what we need, and how you will fit into the team. If this description resonates with you and you don’t feel you have all the right skills, that’s ok!

Ideally you will have

• Previous experience of owning, maintaining, and enhancing software products used by data scientists and supporting data scientists in their development needs
• Familiarity with algorithms, data structures, and performance optimization
• Programming experience in full stack software development
• Intimately familiar with a language typically used by data scientists, e.g. Python, R, MATLAB, Julia
• Experience in logic development, unit tests, code review, core programming of backend software
• Good knowledge of software development best practices including testing, continuous integration, and DevOps tools
• Familiarity with agile software development lifecycle (scrum, Kanban, etc.)
• Some familiarity with machine learning algorithms (classification, regression) and processes (how to build models, assess their goodness of fit, etc.)
• Some background with real analysis, linear algebra, numerical approximation and optimization techniques, and probability theory
• Attention to clarity of code, ease of development, and correctness of implementations

Bonus points for any of the following

The Team

This high-profile team work on ground-breaking problems in health outcomes across disease areas including Ophthalmology, Oncology, Neurology, Chronic diseases (such as diabetes), and a variety of very rare conditions. The team works hand-on-hand with Statisticians, Epidemiologists and disease area experts across the wider global Real-World Solutions group, leveraging a vast variety of anonymous patient-level information. The data encompasses IQVIA’s access to over 530 million anonymised patients as well as bespoke, custom partnerships with healthcare providers and payers.

Why Join?

Those who join us become part of a recognized global leader still willing to challenge the status quo to improve patient care. In RWS, you will have access to the most cutting-edge technology, the largest data sets, the best analytics tools and, in our opinion, some of the finest minds in the Healthcare industry.

We thank all applicants for their interest; however, only those selected for an interview will be contacted

IQVIA is a strong advocate of diversity and inclusion in the workplace. We believe that a work environment that embraces diversity will give us a competitive advantage in the global marketplace and enhance our success. We believe that an inclusive and respectful workplace culture fosters a sense of belonging among our employees, builds a stronger team, and allows individual employees the opportunity to maximize their personal potential.

IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at