Machine Learning Engineer, Predictive Analytics, RWAS Technology (R1011882) in London, UK at IQVIA™

Date Posted: 5/29/2018

Job Snapshot

  • Employee Type:
    Full-Time
  • Location:
    London, UK
  • Job Type:
    Consulting
  • Experience:
    Not Specified
  • Date Posted:
    5/29/2018
  • Job ID:
    R1011882

Job Description



Machine Learning Engineer

Predictive Analytics, RWAS Technology


This is an exciting role for a commercially experienced data-science software developer who likes tackling tough problems of a statistical nature. You will be an integral part of the predictive analytics team, advancing the team’s analysis and modelling stack such that projects can be executed more efficiently and with improved accuracy. You should want to automate your job away, so that repeated tasks are scalable, and fresh challenges and novel approaches can be pursued. Strong communication skills across disciplines is essential in order to collaboratively plan and execute end-to-end solutions with a range of experts including data scientists, software engineers, product owners and system architects.




Day-to-day responsibilities include:

  • Supporting other data scientists in the use of the systems you build for the successful delivery of machine-learning client projects.
  • Working closely with data scientists and software engineers to automate tasks and roadmap improvements for project design and execution.
  • Carrying out a variety of product development initiatives, from requirements gathering to prototyping.
  • Supporting the on-going build and maintenance of a cloud-based machine learning platform for project delivery and product development.



Our ideal candidate: Experience

  • Commercial software development experience within a company for which data was at the heart of the business.
  • Experience in the design and delivery of multiple projects involving advanced statistical methods (preferably machine learning) in an academic and/or commercial setting.
  • Commercial experience in the exploration, cleansing, and transformation of large and messy datasets, along with experience working with very large/complex SQL or distributed database systems.
  • Familiarity with Agile methodologies, such as Scrum or Kanban, as well as software development practices such as Continuous Integration, Test-Driven Development and DevOps.



Our ideal candidate: Tech Skills

  • Advanced level programming skills in Python, ideally developed from experience working on long-term commercial projects, including significant experience using SciPy and machine-learning packages (Numpy, Pandas, scikit-learn, etc) for the development of maintained components. Additional experience using PySpark a big plus.

  • Ability to integrate and scale solutions that involve large data sources in SQL databases and/or distributed systems such as Hadoop, as well as considerable experience deploying at scale on cloud technologies such as AWS, GCP, Azure.

  • A set of software-development values that ensures high-quality, readable and maintainable code is produced within an open and collaborative environment.

  • A pragmatic approach in scope and design, seeking simple iterative solutions wherever possible to shorten the time-to-value of work. 




Bonus points for:

  • Knowledge of supervised machine learning methods, such as regularised regressions, ensemble tree classifiers (e.g. xgboost), Support Vector Machines, deep learning, etc.
  • Additional experience developing in C++, R, Java, Scala, Java, JavaScript, or advanced ability user of shell scripting commands (grep, sed, awk, etc).

  • A demonstrable interest (e.g. public GitHub repo, or online course completion) in one of the following machine learning libraries (or equivalents): TensorFlow, Spark MLLib or CRAN packages for machine learning.

  • Knowledge of healthcare / life science issues involving Real-World Evidence.


We are looking for a confident, innovative and intellectually curious data-science software developer to join our London-based Predictive Analytics team. This is an exciting opportunity to work in one of the world's leading Real-World Insights teams, with access to the world’s largest set of healthcare information and the latest technology platforms and analytics software.




The Team


“Big Data” is changing the way that the healthcare world operates and, now more than ever, the key to better patient outcomes is through better use of technology, seamlessly integrated information and analytics. Our Predictive Analytics team within the Real-World & Analytics Solutions (RWAS) Technology division is a fast-growing group of collaborative, enthusiastic, and entrepreneurial individuals. In our quest to harness the value of Real World Evidence (RWE), we are at the centre of IQVIA’s pursuit of machine learning and cutting-edge statistical methods to advance healthcare. Our efforts improve retrospective clinical studies, under-diagnosis of rare diseases, personalised treatment response profiles, disease progression predictions, and clinical decision-support tools.


You will join this high-profile team to 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 Predictive Analytics team works hand-in-hand with statisticians, epidemiologists and disease area experts across the wider global RWE Solutions team, 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.




We invite you to join IQVIA™.


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.



Job ID: R1011882